CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Net Renal Drug Secretion Critically Requires Significant Contribution of Basal Uptake Transporters: Perspective From Mechanistic Kidney Model Simulation and Integration of Clinical and In Vitro Data","authors":"Toshiaki Tsuchitani,&nbsp;Masatoshi Tomi,&nbsp;Yuichi Sugiyama","doi":"10.1002/psp4.70336","DOIUrl":"10.1002/psp4.70336","url":null,"abstract":"<p>Renal clearance (CL_<i>r</i>) is the net outcome of glomerular filtration, secretion, and reabsorption, which makes the quantitative requirements for net renal secretion difficult to infer from simple clearance models. Using a mechanistic kidney model (MechKiM), we simulated blood-referenced CL_<i>r</i> (CL_<i>r,b</i>) for 30,000 virtual drugs (neutral, monoanionic, and monocationic) under continuous infusion and across all eight combinations of three proximal-tubule transporters (basal uptake, basal efflux, apical efflux). Net renal secretion (clearance ratio CR = CL_<i>r,b</i>/(<i>f</i>_<i>u,b</i> × GFR) &gt; 1.5, where <i>f</i>_<i>u,b</i> and GFR are the unbound fraction in blood and the glomerular filtration rate) occurred only when basal uptake transport was present; apical efflux alone produced proximal secretion that was offset by downstream reabsorption, yielding no net secretion. In the scenario where all three transporters were present within the MechKiM framework, five quantitative criteria distinguished secretion-type drugs, including basal transporter-mediated uptake (&gt; 4.2 L/h) and its fractional contribution to total basal uptake (&gt; 94% for non-cationic compounds). Clinical CL_<i>r,p</i> (plasma-referenced CL_<i>r</i>) versus in vitro uptake-transporter data (<i>n</i> = 217) and published MechKiM-PBPK models (<i>n</i> = 19, typically assigning &gt; 99% of basal uptake to transporters) were consistent with these findings. A sufficient contribution of basal uptake transporters in the proximal tubule is essential for observing net renal secretion. This study provides quantitative, in vitro-translatable criteria to anticipate renal secretion and transporter-mediated drug–drug interaction liability.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70336","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-Based Tacrolimus Dosing Using the Population Pharmacokinetic Analysis Under the Nirmatrelvir/Ritonavir Therapy","authors":"Kotaro Itohara,&nbsp;Yoshiki Katada,&nbsp;Takahiro Hatayama,&nbsp;Toshiaki Igarashi,&nbsp;Jiro Ogura,&nbsp;Tomoki Fukushima,&nbsp;Shingo Yamazaki,&nbsp;Masato Homma,&nbsp;Takashi Niwa,&nbsp;Yoshiaki Kato,&nbsp;Nanae Yamamoto,&nbsp;Hideyuki Terazono,&nbsp;Hideo Shiohira,&nbsp;Tomohiro Terada,&nbsp;Ikuko Yano","doi":"10.1002/psp4.70341","DOIUrl":"10.1002/psp4.70341","url":null,"abstract":"<p>Nirmatrelvir/ritonavir is an antiviral agent used against severe acute respiratory syndrome coronavirus 2 infection and inhibits cytochrome P450 3A and P-glycoprotein. The concomitant use of nirmatrelvir/ritonavir markedly increases tacrolimus blood concentrations. Although temporary discontinuation or dose reduction of tacrolimus at the start of nirmatrelvir/ritonavir therapy is recommended in the package insert of this drug, little information is available on tacrolimus dosage adjustment when restarting. In this study, tacrolimus blood concentrations before and after nirmatrelvir/ritonavir co-administration were collected from national university hospitals across Japan, and population pharmacokinetic analysis was performed to develop an optimal dosing strategy for tacrolimus. In total, 83 tacrolimus blood concentrations from 15 patients were used in the analysis. First, the individual pharmacokinetic parameters of tacrolimus before the intake of nirmatrelvir/ritonavir were estimated by post hoc Bayesian analysis using previously reported population pharmacokinetic parameters based on a two-compartment model. The change in the relative bioavailability and clearance of tacrolimus after nirmatrelvir/ritonavir administration was estimated by population pharmacokinetic modeling. Consequently, tacrolimus clearance was considered almost completely inhibited, and relative bioavailability increased 7.99-fold with the co-administration of nirmatrelvir/ritonavir. The optimal dosing strategy based on the simulation was to withhold tacrolimus during nirmatrelvir/ritonavir therapy and to restart tacrolimus at 10% of the baseline dose on day 1, followed by 20% on day 3% and 50% on day 6, and increased to 100% on day 9 after discontinuation of nirmatrelvir/ritonavir, respectively. The proposed strategy will contribute to a safe and effective tacrolimus therapy when used in combination with nirmatrelvir/ritonavir.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Dosing of Tacrolimus in Liver Transplantation: Integrating Donor-Recipient CYP3A5 Pharmacogenomics and Drug Interactions","authors":"Virunya Komenkul,&nbsp;Prawat Chantharit,&nbsp;Piyawat Komolmit,&nbsp;Bunthoon Nonthasoot,&nbsp;Athaya Vorasittha,&nbsp;Anapat Sanpavat,&nbsp;Sirinporn Suksawatamnuay,&nbsp;Chandramouli Radhakrishnan,&nbsp;Thitima Wattanavijitkul","doi":"10.1002/psp4.70339","DOIUrl":"10.1002/psp4.70339","url":null,"abstract":"<p>Tacrolimus dosing in liver transplantation is complicated by a narrow therapeutic index and high <i>CYP3A5</i> genetic variability. While saturable Michaelis–Menten kinetics can explain nonlinearities, identifying saturable parameters from routine clinical data remains challenging. This study aimed to determine the optimal structural model and develop a precision dosing algorithm. A population pharmacokinetic analysis was conducted in 114 patients, yielding 1989 observations. <i>CYP3A5</i> genotypes were determined for both recipients and donors. Using Phoenix NLME, we rigorously compared linear versus Michaelis–Menten elimination structures. Stepwise covariate modeling was conducted to quantify the impact of genetic, physiological, and pharmacological factors, followed by Monte Carlo simulations to optimize dosing. A conventional two-compartment model adequately described the data without requiring a Michaelis–Menten structure. The combined <i>CYP3A5</i> genotype exhibited a distinct stepwise reduction in apparent clearance from the homozygous expressor to the non-expressor group. Fluconazole emerged as a major inhibitor, reducing clearance by 33%, whereas prednisolone showed modest induction. Hemoglobin displayed a significant inverse relationship with clearance. Crucially, incorporating the daily dose as a covariate on clearance effectively captured the apparent nonlinear disposition. Simulations confirmed that fluconazole-treated patients require substantially lower doses (1.5–3.0 mg every 12 h) compared with fluconazole-free patients (2.5–7.0 mg every 12 h). A dose-dependent two-compartment model offers a basis for model-informed dose selection, addressing reported nonlinearities through physiological covariates. We provide a model-informed dosing algorithm that accounts for combined recipient/donor genetics and drug interactions, which may improve target attainment in liver transplant populations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148879409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Mechanistic Modeling of S-531011, a Humanized Anti-CCR8 Monoclonal Antibody, for Prediction of CCR8 Receptor Occupancy in Human Tumor Tissues","authors":"Daichi Yamaguchi,&nbsp;Wataru Nogami,&nbsp;Yudai Sonoda,&nbsp;Hitomi Morihara,&nbsp;Takayuki Katsube","doi":"10.1002/psp4.70332","DOIUrl":"10.1002/psp4.70332","url":null,"abstract":"<p>Semi-mechanistic modeling approaches could be useful for optimal dose selection, and one of the approaches is pharmacokinetic (PK)/receptor occupancy (RO) model for monoclonal antibodies. This study aimed to develop a semi-mechanistic PK/RO model of S-531011, a humanized monoclonal antibody against human CCR8 and under development for the treatment of solid tumors, in order to predict human RO values in tumor tissues which are not available in the ongoing clinical study. The model consists of three compartments (central, peripheral, and tumor) and includes four compartments (S-531011, CCR8, and complexes 1/2 within each compartment). The model parameters were set using available clinical data, non-clinical data, and physiological information. The human PK/RO model was developed by refinement of a mouse PK/RO model. The time courses of RO values in the tumor compartment were simulated using the model, together with a conservative condition of slow transfer of S-531011 from blood to the tumor. It was predicted that the RO value in the tumor compartment at 21 days (trough after the third dose after administration of S-531011 80 mg) would be maintained at &gt; 90% under typical model assumptions. Under the conservative condition, the RO value in the tumor after administration of S-531011 800 mg would remain at &gt; 90%. The developed semi-mechanistic PK/RO model could predict human RO values of S-531011 in tumor tissues. Under typical model assumptions, simulations suggested the dose range of 80–800 mg of S-531011 every 3 weeks would achieve &gt; 90% RO in tumor tissues.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmaceutical Industry Perspectives on Exposure-Response Confounding in Large Molecule Therapeutics: Results From an IQ Consortium Survey","authors":"Engie Salama,&nbsp;Manisha Lamba,&nbsp;Sihem Ait-Oudhia,&nbsp;Ellen Wang,&nbsp;Dorothee Semiond,&nbsp;Zhang Li,&nbsp;Tao Long,&nbsp;Satyendra Suryawanshi,&nbsp;Leticia Arrington,&nbsp;Wei Gao,&nbsp;Xiaowen Guan,&nbsp;Hugh Giovinazzo,&nbsp;Dale Miles,&nbsp;David C. Turner","doi":"10.1002/psp4.70318","DOIUrl":"10.1002/psp4.70318","url":null,"abstract":"<p>Exposure-response (ER) analyses assessing the efficacy of large molecule therapeutics are often susceptible to confounding, where associations between drug exposure and patient disease severity can obscure true ER signals and complicate dose selection. To better understand the pharmaceutical industry perspectives on this challenge, the IQ consortium conducted a comprehensive survey targeting clinical pharmacologists, pharmacometricians, and statisticians. The survey, completed by 125 individuals from 23 pharmaceutical companies, aimed to assess awareness, perceived prevalence, mitigation strategies, and the overall role of ER analyses in the context of confounding. Results revealed strong industry awareness, with 88.0% of respondents acknowledging the relevance of ER confounding. However, uncertainty regarding its pervasiveness persists, particularly in non-oncology settings (31.2% unsure). A consensus emerged on the value of dose-ranging study designs as an effective mitigation strategy (81.6% agreement). In contrast, the use of advanced statistical methods for causal inference is inconsistent (45.6% usage), and confidence in their reliability is mixed, with 32.8% of respondents expressing uncertainty and most others rating them only moderate or somewhat reliable. While the majority agreed that confounded ER analyses should be interpreted with caution (79.2%), opinions diverged regarding their value in decision-making when dose-ranging data is insufficient. This uncertainty, coupled with a recognized need for additional alignment with health authorities, led to a call for best-practice guidance (92.8% view as valuable). Overall, the survey findings highlight a need for an industry-wide common approach and the development of clear frameworks to manage and interpret ER confounding for large molecule therapeutics.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13542575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Practical Alternative to Refine the Estimate of fmCYP3A4 and Evaluate Drug–Drug Interaction Potential for Ziftomenib Using PBPK Modeling to Inform Labeling","authors":"Ian E. Templeton,&nbsp;Chara Litou,&nbsp;Hannah M. Jones,&nbsp;Julie Mackey Ahsan,&nbsp;Marilyn Tabachri,&nbsp;Mollie Leoni,&nbsp;Amitava Mitra","doi":"10.1002/psp4.70327","DOIUrl":"10.1002/psp4.70327","url":null,"abstract":"<p>Accurate physiologically based pharmacokinetic (PBPK) simulation of drug–drug interaction (DDI) potential requires estimation of the relative contribution of the impacted pathway. While fraction metabolized by CYP enzymes is usually estimated using dedicated clinical DDI studies with strong CYP inhibitors, this approach might not be available in some situations. In the case of the menin inhibitor, ziftomenib, a dedicated DDI study in healthy subjects was infeasible due to the potential for toxicity associated with the mechanism of action, so an alternate approach was used. AML patients are typically immuno-compromised and hence are at a high risk of fungal infections. For this reason, co-administration of azole antifungals (moderate and strong CYP3A4 inhibitors) was permitted in Clinical Study KO-MEN-001. The PK data obtained in the presence or absence of CYP3A4 inhibitors was used to refine the estimate of ziftomenib fm_CYP3A4 to 60%–70%. Once refined, the model was applied to predict the victim DDI liability of ziftomenib in the presence of CYP3A4 inhibitors and inducers. Moderate or weak interaction (2.6-fold and 1.4-fold increase in AUC) was predicted with itraconazole and isavuconazole, respectively. Approximately 80% reduction in ziftomenib AUC was predicted with rifampicin. Ziftomenib was predicted to be a weak CYP3A4 inhibitor, causing a 1.9-fold increase in midazolam exposure. In the absence of data from dedicated DDI clinical trials, these results were used to support regulatory interactions with the US FDA regarding concomitant administration of ziftomenib with other medications such as CYP3A4 modulators. These modeling results ultimately supported a range of DDI language in ziftomenib label.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148873204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjoint and Unrolled Automatic Differentiation for Laplace-Approximated Likelihoods in Population PK and PK/PD Models","authors":"Guido H. Jajamovich,&nbsp;William Holmes,&nbsp;Chih-Wei Lin,&nbsp;Khamir Mehta","doi":"10.1002/psp4.70333","DOIUrl":"10.1002/psp4.70333","url":null,"abstract":"&lt;p&gt;Maximum likelihood estimation in population pharmacokinetic/pharmacodynamic (PK/PD) nonlinear mixed-effects (NLME) models targets fixed-effect, interindividual-variability, and residual-variability parameters through a marginal likelihood that integrates each subject's contribution over individual random effects. Because these subject-level integrals are usually unavailable analytically, Laplace estimation approximates each contribution using a second-order Taylor expansion around the empirical Bayes estimate (EBE; conditional posterior mode of the individual's random effects), making EBE estimation and curvature evaluation recurring tasks during objective function value (OFV) and derivative evaluation. Finite differences (FD) are commonly used for these derivative calculations but are costly and step-size sensitive. We developed automatic differentiation (AD)-based methods for Laplace NLME estimation and compared three ways to account for EBE sensitivities during updates: FULL-implicit uses the EBE mode equations, FULL-unroll differentiates through the Newton steps used to find EBEs, and STOP omits EBE sensitivity during outer differentiation. In 100 matched starts for a synthetic one-compartment PK example with absorption/elimination ambiguity, FULL-implicit, FULL-unroll, and FD matched the lowest OFV within &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;4&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {10}^{-4} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; OFV units, whereas STOP had maximum &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mtext&gt;ΔOFV&lt;/mtext&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;1.9&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ Delta mathrm{OFV}=1.9 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; relative to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;OFV&lt;/mi&gt;\u0000 &lt;mi&gt;min&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1594.62&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{OFV}}_{mathrm{min}}=-1594.62 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. Median wall times were 0.045, 0.108, 0.068, and 0.758 s for FULL-implicit, FULL-unroll, STOP, and FD, respectively, making FULL-implicit about 17-fold faster than FD. In 10 runs on public warfarin PK/PD data using the ODE representation, FULL-implicit achieved a lower best OFV than FD (1624.02 vs. 1628.60) and was 36-fold faster by median wall time (0.45 vs. 16.3 min). In both scenarios, FULL-implicit provided a faster AD-bas","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148873213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the Mechanisms of Statin–Ezetimibe Drug Combinations Using Boolean Logical Modeling and Transcriptomic Data","authors":"Rui-Sheng Wang,&nbsp;Matteo Pedrelli,&nbsp;Osman Ahmed,&nbsp;Garagnani Paolo,&nbsp;Paolo Parini,&nbsp;Joseph Loscalzo","doi":"10.1002/psp4.70329","DOIUrl":"10.1002/psp4.70329","url":null,"abstract":"<p>Drug Combinations offer increased therapeutic efficacy and reduced toxicity compared with single agents. Understanding a drug combination's mechanisms of action (MoA) can provide important insights into therapeutic efficacy. The MoA of many FDA-approved drugs, however, often remains unclear. To decipher the underlying molecular mechanisms of drugs used alone and in combination, we investigated the combination of a statin (atorvastatin or simvastatin) plus ezetimibe using drug-treated RNA-seq transcriptome data from the human hepatocyte-like SOAT2-only-HepG2 cells and from liver biopsies of non-obese normolipidemic patients with uncomplicated cholesterol gallstone disease in the Stockholm Study. We proposed a novel Boolean logical modeling framework to simulate the MoA of a drug combination using fourteen two-variable Boolean models. Thereafter, a pattern matching approach was applied to associate drug-induced differentially expressed genes with the idealized differential expression templates derived from Boolean models. We found 1560 and 565 genes differentially expressed in at least one treatment condition in SOAT2-only-HepG2 cells and liver biopsies, respectively. Our analysis revealed both expected and novel combinatorial modes of the statins and ezetimibe. We mapped the downstream genes of each combinatorial mode to the human protein–protein interactome and obtained underlying pathways, which are important for understanding the therapeutic effects of the drug combinations. Functional enrichment and disease-association analyses of the downstream genes also provide critical insights into the additional therapeutic actions of the drugs. Our study demonstrates that drug-induced transcriptomes, integrated with the human interactome, are informative in deciphering the MoA of drug combinations using Boolean logical modeling.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13532064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148863840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transporter-Drive Interstitial Tissue Exposure and Pharmacodynamic Response of Meropenem in Sepsis: A Mechanistic PBPK Modeling Study","authors":"Laura Ben Olivo,&nbsp;Jessica Luisa Silva de Lemos,&nbsp;Vinicius Jardim Rodrigues,&nbsp;Bibiana Verlindo de Araújo","doi":"10.1002/psp4.70335","DOIUrl":"10.1002/psp4.70335","url":null,"abstract":"<p>A whole-body mechanistic PBPK model for meropenem (MPN) was developed in PK-Sim and validated using published plasma and tissue concentration–time data in healthy volunteers and critically ill patients. Renal elimination was implemented as glomerular filtration plus active tubular secretion (basolateral uptake via OAT3 with apical efflux), and non-renal clearance via DHP-mediated hydrolysis. The model was scaled to sepsis or septic shock by incorporating disease-specific physiological changes and optimizing OAT3 activity and tissue permeability to reproduce observed variability. Simulations in a virtual septic population assessed unbound interstitial concentrations in clinically relevant tissues under ILAS-recommended typical and maximum dosing MPN regimens. Antibacterial effect against <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i> was evaluated using a published PK/PD model driven by simulated unbound tissue concentrations. The PBPK model reproduced observed plasma profiles in healthy volunteers and captured plasma and subcutaneous interstitial exposure in sepsis. Simulations showed clear dissociation between plasma and interstitial exposure and marked tissue-specific heterogeneity. Predicted clearance increased in sepsis (augmented renal clearance) and decreased in septic shock (impaired renal function and secretion). Although maximum dosing increased plasma and tissue exposure, PD simulations indicated effects were already near the plateau with standard dosing, yielding minimal additional antibacterial benefit from routine dose escalation. Transporter-informed PBPK/PD modeling explains dynamic, severity-dependent changes in meropenem clearance and tissue exposure in sepsis, highlights limitations of plasma-only assessment, and supports individualized, mechanism-informed optimization rather than universal dose scaling.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148873180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generalization of the Ternary Binding Model to Membrane-Confined Systems With Finite Copy Number","authors":"Hamid Bellout,&nbsp;Angela Li,&nbsp;Dean Bottino,&nbsp;Konstantin Piatkov","doi":"10.1002/psp4.70323","DOIUrl":"https://doi.org/10.1002/psp4.70323","url":null,"abstract":"&lt;p&gt;The standard Douglass ternary binding model (TBM) for three-body equilibria assumes a well-mixed, three-dimensional solution. When applied to bispecific T-cell engagers (BiTEs), however, the productive trimeric complex forms not in bulk solution but within a nanoscale membrane synapse with finite receptor copy numbers. We present a generalization of the TBM to membrane-confined systems that replaces the macroscopic bulk volume with a coarse-grained reactive contact volume defined by synapse geometry and microvillus topology, and extends the deterministic equilibrium to a stochastic description via the chemical master equation. The framework preserves the original algebra while restoring its representational capacity for the regime in which therapeutic activity occurs. A key finding is that conventional bulk mapping places the system in the affinity-limited regime, where antigen density is mathematically inert and the TBM predicts identical dose–response regardless of target expression. Membrane confinement shifts effective antigen concentration by six orders of magnitude—from &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;∼&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ sim {10}^{-3} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; nM to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;∼&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ sim {10}^3 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; nM—restoring antigen density as a governing variable for trimer formation. Using blinatumomab (anti-CD19 BiTE) as a case study, we introduce the &lt;i&gt;absolute formation dose&lt;/i&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;mo&gt;*&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ dleft({N}^{ast}right) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;: the drug concentration required to produce a fixed number of ternary complexes sufficient for T-cell activation. This metric replaces the conventional &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ T{F}_{50} $$&lt;/annotation&gt;\u0000 ","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}