CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Target-Engagement Prediction From Molecular Structure, In Vitro and Animal Data: A Tutorial for Early Assessment of Clinical Drug Exposure and Therapeutic/Adverse Potential Using the ANDROMEDA and PLATO Software on the InSilicoTrials Platform","authors":"Niccolò Totis,&nbsp;Fianne Sips,&nbsp;Simona Catozzi,&nbsp;Marc-Antonio Bisotti,&nbsp;Pauline Bambury,&nbsp;Sofia Stathopoulos,&nbsp;Vincenzo Carbone,&nbsp;Luca Emili,&nbsp;Mario Torchia,&nbsp;Valentina Belgiovine,&nbsp;Fulvio Ciriaco,&nbsp;Orazio Nicolotti,&nbsp;Urban Fagerholm,&nbsp;Candice N. Baker,&nbsp;J. Matthew Mahoney,&nbsp;Daniel Röshammar,&nbsp;the CARDIOVERSE Project","doi":"10.1002/psp4.70353","DOIUrl":"10.1002/psp4.70353","url":null,"abstract":"<p>The target-engagement predictor, implemented within the InSilicoTrials cloud-based, AI-powered platform for model-informed drug discovery and development, enables early exploration of time-dependent pharmacological responses. Predictions can be generated using inputs ranging from molecular structure to in vitro, animal, and clinical data. Scaling and translation across species are supported, accounting for differences in body size and plasma protein binding. This tutorial demonstrates the application of the target-engagement predictor for benchmarking and decision-support in the evaluation of new drug candidates. Pharmacokinetic (PK) and pharmacodynamic (PD) data for carvedilol, ivabradine, and sunitinib were used as illustrative examples. The ANDROMEDA and PLATO software were used to predict human clinical PK and target affinity parameters, respectively, based on molecular SMILES representations of the drugs. These predictions were subsequently incorporated into longitudinal PKPD simulation models. User-friendly input wizards and interactive results dashboards were developed to facilitate clinical trial simulation for broader project teams, without requiring programming expertise. Predictions derived from molecular structure were compared with data generated from in vitro, rat, and clinical experimental settings. The target-engagement predictor enables integration of heterogenous data sources to support drug development decisions, including early benefit–risk assessment and dose selection for Phase 1 studies. Direct prediction based on molecular structure alone represents a promising approach for early-stage screening and evaluation of new drug candidates.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191842","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":"Longitudinal Modeling of Cardiometabolic Dysfunction in Patients With Type 2 Diabetes Using Item Response Theory","authors":"Chengli Yu,&nbsp;Hanna Kunina,&nbsp;Gustaf J. Wellhagen,&nbsp;Maria C. Kjellsson","doi":"10.1002/psp4.70349","DOIUrl":"https://doi.org/10.1002/psp4.70349","url":null,"abstract":"&lt;p&gt;Cardiometabolic dysfunction in patients with type 2 diabetes (T2D) is reflected by multiple risk factors and cannot be fully characterized by any single biomarker. Item response theory (IRT) provides a framework for integrating observed risk factors into latent measures of underlying cardiometabolic dysfunction. Routinely collected measurements of systolic and diastolic blood pressure (sBP and dBP), high- and low-density lipoproteins (HDL and LDL), triglycerides (TG), HbA1c, and BMI from 33,396 adults newly diagnosed with T2D were analyzed, with biomarkers categorized into four ordered levels. A two-dimensional model described the data better than a unidimensional, identifying a blood pressure-related latent variable (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;&lt;sub&gt;1&lt;/sub&gt;) primarily informed by sBP and dBP, and a metabolic latent variable (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;&lt;sub&gt;2&lt;/sub&gt;) primarily informed by HDL, HbA1c, TG, and BMI, while LDL showed low discrimination on both dimensions. At baseline, age at T2D diagnosis was associated with both latent variables, with opposing effects of 0.02 for &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;&lt;sub&gt;1&lt;/sub&gt; and −0.07 for &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;&lt;sub&gt;2&lt;/sub&gt;. Sex was associated with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;&lt;sub&gt;2&lt;/sub&gt;, with women scoring 0.43 units lower than men on the latent variable scale. Longitudinally, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;&lt;sub&gt;1&lt;/sub&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ψ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ psi $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;sub&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sub&gt; showed post-baseline offsets of −0.10 and −0.22, respectively, followed by separately estimated annual slopes of approximately −0.03 year&lt;sup&gt;−1&lt;/sup&gt;. HbA1c exhibited a distinct trajectory with an initial post-baseline improvement ","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177120","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":"Reply to “Aflibercept 8 mg: From Ocular Clearance to Pharmacodynamic Duration”","authors":"Sébastien Bihorel,&nbsp;Joannellyn Chiu,&nbsp;Jason Chittenden,&nbsp;Thomas Eissing,&nbsp;Kenneth C. Turner,&nbsp;Joachim Höchel,&nbsp;Zhongqing Will He,&nbsp;John D. Davis,&nbsp;Lutz O. Harnisch","doi":"10.1002/psp4.70350","DOIUrl":"https://doi.org/10.1002/psp4.70350","url":null,"abstract":"<p>We thank Veritti and colleagues for commenting on our publication “Integrated Population Pharmacokinetic Analysis of Intravitreal Aflibercept Drug Products” in the Letter to the Editor “Aflibercept 8 mg: From Ocular Clearance to Pharmacodynamic Duration” and agree on their thoughts to extend our work to pharmacokinetic/pharmacodynamic (PK/PD) modeling of clinical endpoints. As noted, the analysis presented in our manuscript focused on aflibercept PK. One outcome was that the median ocular concentration of free aflibercept at the end of an 8-week dosing interval for aflibercept 2 mg was predicted to be reached 6 weeks later for aflibercept 8 mg. This PK benchmark did not address the proportion of participants maintained on certain dosing intervals. In view of the variability of dosing intervals observed in PULSAR and PHOTON (including cases of participants receiving aflibercept 8 mg up to every 24 weeks) [<span>1</span>], we pursued exposure-response analyses which supported the regulatory review of aflibercept 8 mg in multiple territories and identified ocular clearance (Q_E) and central retinal thickness (CRT) as prognostic factors of dosing interval shortening [<span>1-3</span>]. In these analyses, patients with neovascular age-related macular degeneration, diabetic macular edema, or retinal vein occlusion with fast Q_E or high baseline CRT, which is indicative of greater disease severity, had an increased likelihood of dosing interval shortening [<span>1-3</span>]. Downstream of target binding, there is substantial PK, PD, and pathophysiology-related complexity to arrive at ocular outcome parameters, whether anatomic (CRT) or functional (best-corrected visual acuity [BCVA]). We recently presented a population PK/PD analysis linking predicted ocular concentrations of free aflibercept with CRT reductions and BCVA improvements in aflibercept 8 mg-treated patients in PULSAR and PHOTON [<span>4</span>]. This analysis indicated that, although most patients maintain effective ocular concentrations with ≥ 8-week dosing intervals, a small subset of patients with fast Q_E and high baseline CRT may require more frequent dosing to maintain effective concentrations and clinical efficacy. This analysis aligns well with Verriti's proposal and warrants further work to evaluate whether the extended duration of aflibercept 8-mg effects at ≥ 16-week dosing intervals can be linked to slower Q_E and/or other patient characteristics (e.g., low CRT).</p><p>The analysis reported in the original article entitled “Integrated Population Pharmacokinetic Analysis of Intravitreal Aflibercept Drug Products” was funded by Regeneron Pharmaceuticals, Inc. (Tarrytown, New York) and Bayer AG (Leverkusen, Germany).</p><p>S.B., Jo.C., Ja.C., K.C.T., Z.W.H., J.D.D., and L.O.H. are employees and shareholders of Regeneron Pharmaceuticals, Inc. T.E. and J.H. are employees and shareholders of Bayer AG.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177123","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":"Preclinical Pharmacokinetic-Pharmacodynamic Modeling of Antibody Nanoconjugates for Breast Cancer Treatment","authors":"Anne Rodallec,&nbsp;Cristina Vaghi,&nbsp;Joseph Ciccolini,&nbsp;Raphaelle Fanciullino,&nbsp;Sebastien Benzekry","doi":"10.1002/psp4.70348","DOIUrl":"10.1002/psp4.70348","url":null,"abstract":"<p>Over the past 5 years, the number of antibody–drug conjugates (ADCs) entering the oncology market has surged. By contrast, the clinical use of immunoliposomes—nanoparticles that also employ antibodies as targeting agents (i.e., antibody–nanoconjugates, ANCs)—has yet to receive approval. One possible reason is the limited understanding of the relationship between ANC exposure and efficacy. To address this gap, we developed a pharmacokinetic/pharmacodynamic (PK/PD) model for a docetaxel–trastuzumab ANC. PK and PD studies were conducted in MDA-MB-231 tumor–bearing mice, divided into three treatment groups: (i) control, (ii) free docetaxel + free trastuzumab, and (iii) docetaxel–trastuzumab ANCs. PK data were obtained by fluorescence imaging and captured systemic and tumor distribution of ANCs, while PD data were based on tumor growth measurements. The PK model was built using data from the ANC group, and the PD model was developed across all three treatment groups, with a leave-one-out cross validation strategy for predictive evaluation. Treatment schedule simulations were then performed. Using a population nonlinear mixed-effects (NLME) approach, the final PK model included a central and a tumor compartment, each with independent elimination rates. Tumor growth dynamics were best described by a reduced Gompertz model coupled with a Simeoni resistance model. This model demonstrated strong predictive performance. Using only data from the first two treatment cycles (4 time points) and Bayesian estimation in the test set, it accurately predicted systemic and tumor ANC concentrations across the subsequent four treatment cycles, with a relative error (RE) below 15%. Prediction precision was further improved (RE &lt; 5%) when data from one additional treatment cycle were incorporated. Similar performance was achieved for PD data, with tumor volume predictions exhibiting RE &lt; 15% and RE &lt; 7.5% when implementing data from two and three treatment cycles, respectively. The model was then applied to simulate alternative treatment schedules. Interestingly, while systemic exposure remained comparable across regimens, every-5-day administration yielded the greatest tumor exposure compared to daily, weekly, and every-35-day dosing (mean ± SD tumor AUC: 55.4 ± 10.9 mg/kg day vs. 47.6 ± 9.38 mg/kg day, 51.4 ± 11.5 mg/kg day, and 44.6 ± 10.0 mg/kg day, respectively, one-way ANOVA, <i>p</i> &lt; 0.001), highlighting the importance of dosing frequency. Overall, these results illustrate how PK/PD modeling can refine intra-tumor PK understanding and support preclinical development of innovative formulations such as ANCs.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13655776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169633","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":"Integrating a Tumor Size-Overall Survival Model Into a Spatial Quantitative Systems Pharmacology Framework for Antibody-Drug Conjugates in Non-Small Cell Lung Cancer","authors":"Mehdi Nikfar,&nbsp;Richard Franzese,&nbsp;Herbert Struemper,&nbsp;Brett Howell,&nbsp;Murad Melhem","doi":"10.1002/psp4.70352","DOIUrl":"10.1002/psp4.70352","url":null,"abstract":"<p>Antibody-drug conjugate (ADC) development in solid tumors faces efficacy and safety challenges from heterogeneous target expression, limited tumor penetration, and off-target payload exposure. We developed a spatial quantitative systems pharmacology (QSP) framework integrating ADC-analyte physiologically based pharmacokinetics (PBPK), a Krogh-cylinder tumor model, and a non-small cell lung cancer (NSCLC) tumor size-overall survival (TS-OS) relationship developed without ADC-treated populations. Applied to trastuzumab deruxtecan (T-DXd) in HER2-mutant NSCLC and datopotamab deruxtecan (Dato-DXd), a TROP2-directed ADC, in EGFR-mutated NSCLC, all parameters came from literature or independent assays; only nonspecific antibody uptake and unconjugated-payload hepatic clearances were estimated from population-level T-DXd serum PK. Using a common structure and virtual-patient framework with independently informed ADC-specific inputs, simulations generated PK, objective response rate (ORR), disease control rate (DCR), and exploratory OS trends consistent with clinical data. Heterogeneous tumor trajectories emerged without fitting individual trajectories, adding ad hoc response compartments, or retuning tumor growth solely to reproduce ORR/DCR. Tumor growth kinetics and target-expression characteristics, represented by histological score and antigen-positive cell fraction, were associated with response, but responder/non-responder distributions overlapped, indicating limited discrimination and need for predictive biomarkers. The TS-OS sub-model reproduced median OS and survival trends, supporting transportability despite absent individual-level tumor and survival data. Tumor growth rate was the strongest survival-associated input, consistent with TS-OS covariate structure. Across doses, 5.4 mg/kg Q3W T-DXd and 6 mg/kg Q3W Dato-DXd were preferred based on predicted efficacy and off-tumor tissue payload exposure, consistent with approved doses. The framework supports ADC design and dose evaluation despite limited individual-level data.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135286","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":"Causal Exposure-Response Analysis: Estimands, Time-Varying Confounding, and the Parametric g-Formula","authors":"Nathan Braniff","doi":"10.1002/psp4.70346","DOIUrl":"https://doi.org/10.1002/psp4.70346","url":null,"abstract":"<p>Exposure-response (ER) analysis often informs dose selection in clinical drug development, but the underlying causal assumptions are rarely made explicit. Exposure metrics commonly used in practice can have limitations: time-to-event metrics are outcome-dependent and can be causally incoherent; steady-state metrics can introduce immortal-time bias; and early metrics, while defensible, require unstated assumptions about later exposure or must be interpreted narrowly. Beyond metric selection, challenges persist even with ideal early or time-varying exposure metrics, as dose randomization protects against confounding only for the component of exposure variability driven by the assigned dose. On-treatment clinical variables can affect both exposure and response, inducing time-varying confounding that neither randomization, baseline adjustment, nor naive time-varying covariate adjustment can address. In this work, a directed acyclic graph (DAG) is used to formalize the causal structure and define the average causal exposure-response curve (ACERC) as the target estimand for ER analysis under time-varying confounding. The parametric g-formula is adapted to target this causal estimand and required assumptions are stated explicitly, including key consistency and no-multiple-versions-of-treatment (NMVT) assumptions, with implications for defining exposure metrics. Population pharmacokinetic (popPK) modeling serves an important role beyond computing exposure metrics, partially addressing the no-measurement-error assumption and enabling g-formula implementation via existing software. Two oncology-motivated simulation scenarios of dose-mediated and PK-mediated confounding illustrate the approach. A simulation study shows the g-formula recovers causal ER estimates where standard methods are inaccurate under the specified time-varying confounding. The two-step workflow combining popPK modeling with existing g-formula software is presented as a practical template for further development.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146698","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":"Aflibercept 8 mg: From Ocular Clearance to Pharmacodynamic Duration","authors":"Daniele Veritti,&nbsp;Valentina Sarao,&nbsp;Paolo Lanzetta","doi":"10.1002/psp4.70351","DOIUrl":"10.1002/psp4.70351","url":null,"abstract":"<p>We read with great interest the integrated population pharmacokinetic analysis by Bihorel et al. The authors provide a rigorous and clinically relevant characterization of the extended ocular exposure of aflibercept 8 mg, indicating that it is not explained by dose escalation alone. In particular, the estimated 39.4% reduction in ocular clearance associated with the commercial aflibercept 8 mg drug product represents an important contribution to understanding the greater durability of this formulation [<span>1</span>].</p><p>One point deserves emphasis when interpreting these findings in terms of clinical durability. In the simulations, the median ocular free-aflibercept concentration after aflibercept 8 mg reached the concentration observed at the end of the aflibercept 2 mg every-8-week interval at approximately 14 weeks. This is a pharmacokinetic benchmark, not a validated threshold for VEGF suppression, retinal fluid recurrence, or loss of clinical efficacy.</p><p>Our previous work addressed the complementary pharmacodynamic question. We first developed a mathematical framework relating intravitreal anti-VEGF concentrations to the estimated fraction of unbound VEGF [<span>2</span>]. Using identical clearance assumptions across aflibercept doses, we subsequently estimated that increasing the dose from 2 to 8 mg could support treatment intervals of approximately 12–15 weeks [<span>3</span>]. More recently, we incorporated the preliminary ocular-clearance estimates now reported in full by Bihorel et al. Under those assumptions, the commercial 8-mg formulation was predicted to maintain VEGF suppression for up to approximately 20 weeks [<span>4</span>]. This prediction is consistent with the durability observed in the 96-week PULSAR results: among patients completing the study in the aflibercept 8-mg q16 group, 53% had a last assigned dosing interval of at least 20 weeks [<span>5</span>].</p><p>Bihorel et al. appropriately leave the mechanism underlying the product-specific reduction in clearance unresolved. Differences in protein concentration, formulation composition, rheological properties, injection volume, and initial intravitreal distribution may contribute [<span>4</span>], but these hypotheses require direct experimental testing.</p><p>A useful next step would be to link the population distribution of ocular clearance to VEGF suppression and clinical disease activity. An integrated PK/PD framework could estimate the probability of maintaining adequate suppression at 12, 16, and 20 weeks and assess whether patient characteristics materially alter that probability.</p><p>The authors have nothing to report.</p><p>Daniele Veritti is a consultant to Bayer and Roche. Valentina Sarao is a consultant to Bayer, I-Care, and Roche. Paolo Lanzetta is a consultant to Aerie, Allergan, Annexon, Bausch + Lomb, Bayer, Biogen, Boehringer Ingelheim, EyeBio, Genentech, I-Care, Ocular Therapeutix, Outlook Therapeutics, and Roche.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135302","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":"Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective","authors":"Xinyi Liu,&nbsp;Shuai Shao,&nbsp;Dorothee Semiond,&nbsp;Malidi Ahamadi","doi":"10.1002/psp4.70330","DOIUrl":"10.1002/psp4.70330","url":null,"abstract":"<p>Accurate prediction of time-to-event outcomes is essential for clinical decision-making and model-informed drug development (MIDD). Conventional survival models may inadequately represent complex longitudinal patient trajectories, time-dependent treatments, and evolving biomarker or pharmacokinetic/pharmacodynamic (PK/PD) profiles. Recent advances in machine learning (ML) and deep learning (DL) have enabled dynamic survival models that update individualized risk predictions as new longitudinal information becomes available. This report systematically reviews dynamic survival modeling frameworks, including extensions of Cox proportional hazards (CPH) with time-varying covariates, discrete-time landmarking strategies, random survival forests (RSF), and deep learning approaches such as DeepHit, Dynamic-DeepHit, and neural ordinary differential equations (ODEs). Applications span sepsis detection, cancer prognosis, and neurodegenerative disease progression. Dynamic models generally achieved discrimination comparable to or better than static Cox models when longitudinal data were available; however, calibration was rarely reported, external validation was uncommon, and integration of PK/PD features into dynamic survival frameworks remains underexplored. Integrating exposure–response modeling with dynamic ML/DL may advance precision dosing, support adaptive trial design, and enhance translational relevance in pharmaceutical research. Emerging regulatory frameworks, including ICH M15 and FDA draft guidance on artificial intelligence in drug development, further emphasize clearly defined contexts of use, risk-based model assessment, credibility, and transparent reporting. Dynamic prediction at the intersection of ML/DL, PK/PD, and regulatory science represents a promising path toward clinically deployable models to inform drug development and regulatory decision-making.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135306","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":"Application of an Open-Source Modular Framework for Pharmacokinetic, Pharmacodynamic, and Safety Simulations to Anti-Tuberculosis Drugs","authors":"Marco Siccardi,&nbsp;Nina Nauwelaerts,&nbsp;Raphaëlle Lesage,&nbsp;Diane Lefaudeux,&nbsp;Vanessa Baier,&nbsp;Venetia Karamitsou,&nbsp;Cleo Demeester,&nbsp;Miao-Chan Huang,&nbsp;Alexander Kulesza,&nbsp;Stephan Schaller","doi":"10.1002/psp4.70347","DOIUrl":"10.1002/psp4.70347","url":null,"abstract":"<p>Tuberculosis (TB) treatment requires long-term multidrug regimens where dosing, efficacy, and safety optimization remain challenging. This tutorial establishes an integrated, open-source, modular framework connecting high-throughput PBPK (HT-PBPK) simulations, mechanistic PBPK models, pharmacodynamic endpoints, and safety predictions for anti-TB drugs. Using the Open Systems Pharmacology Suite and ESQhtpbpk (R package), physicochemical parameters were obtained from literature, with QSAR-predicted values supplementing missing experimental data to generate generic PBPK models. Mechanistic models were parameterized for moxidectin, isoniazid, moxifloxacin, bedaquiline, and rifampicin. A quantitative TB disease model of host-pathogen dynamics and PK-PD interactions was integrated to assess efficacy. A cardiac electrophysiology model incorporating ion channel inhibition data was coupled with PBPK predictions for moxifloxacin and bedaquiline. The HT-PBPK framework automated virtual studies across compounds and dosing regimens. Mechanistic models reproduced systemic and local disposition within two-fold of the observed data. The TB disease model predicted treatment response across exposure scenarios. The cardiac module captured the dose-QTc relationship for moxifloxacin and identified metabolite-driven QTc prolongation with bedaquiline, consistent with clinical observations. This open-source, modular framework spans compound screening, mechanistic PK, efficacy, and cardiotoxicity, providing a reproducible tool to support TB drug development, guide dose selection, and inform regulatory decisions.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149105395","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":"Population Pharmacokinetics of Dolutegravir in Plasma and Cerebrospinal Fluid in Adults Living With HIV","authors":"Mohammed W. Ali,&nbsp;Sam Nightingale,&nbsp;Tracy Kellermann,&nbsp;John Joska,&nbsp;Alan Winston,&nbsp;Saye Khoo,&nbsp;Joseph F. Standing,&nbsp;Eric H. Decloedt,&nbsp;Ahmed A. Abulfathi","doi":"10.1002/psp4.70345","DOIUrl":"10.1002/psp4.70345","url":null,"abstract":"<p>Characterizing dolutegravir penetration into the central nervous system is important for optimizing human immunodeficiency virus treatment outcomes, yet population-specific pharmacokinetic data describing cerebrospinal fluid disposition and equilibration kinetics remain lacking. This study characterizes dolutegravir pharmacokinetics in plasma and cerebrospinal fluid to describe the plasma-to-cerebrospinal fluid equilibration rate constant and cerebrospinal fluid-to-plasma pseudo-partition coefficient. Sparse pharmacokinetics samples were collected from 121 antiretroviral therapy-experienced adults who are predominantly overweight or obese (body mass index: 32.2 ± 8.7 kg/m²) receiving daily dolutegravir (50 mg)-based therapy. The model was developed using nonlinear mixed-effects modeling in NONMEM. Plasma dolutegravir pharmacokinetics were best described by a one-compartment model with first-order absorption and elimination. The population estimate for apparent oral clearance was 1.38 L/h/70 kg. Cerebrospinal fluid data were modeled using a sequential approach with a hypothetical effect compartment linked to plasma to estimate equilibration kinetics and relative cerebrospinal fluid exposure at steady-state. Key cerebrospinal fluid penetration parameters included a plasma-to-cerebrospinal fluid equilibration rate constant of 0.38 h⁻¹, corresponding to an equilibration half-life of 1.82 h, and a cerebrospinal fluid-to-plasma pseudo-partition coefficient of 0.0015, indicating 0.15% cerebrospinal fluid penetration relative to plasma. Post hoc estimated area under the concentration-time curve (0–24) demonstrated a moderate inverse correlation with body mass index (<i>r</i> = −0.34), indicating that dolutegravir exposure decreases with increasing body mass index. The model revealed rapid plasma-to-cerebrospinal fluid equilibration kinetics with low steady-state cerebrospinal fluid penetration in this predominantly overweight and obese population.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149080006","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}