Dan Zhang PhD, Wenwen Du PhD, Pengmei Li MS, Wenqian Chen PhD, Xiaoxing Wang PhD
The pharmacokinetic (PK) profiles of linezolid in lung transplant recipients (LTRs) differ from those in other patients. This study aimed to develop a population pharmacokinetic (PopPK) model to evaluate dosage regimens based on various biological covariates and assess the risk of thrombocytopenia. The nonlinear mixed-effects modeling method was employed to establish the PopPK model. Monte Carlo simulations assessed the probability of target attainment (PTA) under different covariates and minimum inhibitory concentration (MIC) ranges. The study included 43 LTRs with 142 linezolid concentration–time data points. The final model identified estimated glomerular filtration rate and tacrolimus trough level as key covariates for apparent clearance. The 300 mg bid regimen maintained peak and trough concentrations within the 2–8 mg/L range, while the 600 mg qd dosing group achieved a PTA greater than 80% across an MIC range of 0.25–1 mg/L. Linezolid Cmin impacted platelet levels, with baseline levels influencing the severity of reduction. This PopPK model offers valuable insights for optimizing linezolid dosing, considering immunosuppressant therapy and thrombocytopenia risk, and serves as a reference for dose selection in post-surgical LTRs.
{"title":"Optimizing Linezolid Dosing in Lung Transplant Recipients: Population Pharmacokinetics, Target Attainment, and Thrombocytopenia Risk Assessment","authors":"Dan Zhang PhD, Wenwen Du PhD, Pengmei Li MS, Wenqian Chen PhD, Xiaoxing Wang PhD","doi":"10.1002/jcph.70153","DOIUrl":"10.1002/jcph.70153","url":null,"abstract":"<p>The pharmacokinetic (PK) profiles of linezolid in lung transplant recipients (LTRs) differ from those in other patients. This study aimed to develop a population pharmacokinetic (PopPK) model to evaluate dosage regimens based on various biological covariates and assess the risk of thrombocytopenia. The nonlinear mixed-effects modeling method was employed to establish the PopPK model. Monte Carlo simulations assessed the probability of target attainment (PTA) under different covariates and minimum inhibitory concentration (MIC) ranges. The study included 43 LTRs with 142 linezolid concentration–time data points. The final model identified estimated glomerular filtration rate and tacrolimus trough level as key covariates for apparent clearance. The 300 mg bid regimen maintained peak and trough concentrations within the 2–8 mg/L range, while the 600 mg qd dosing group achieved a PTA greater than 80% across an MIC range of 0.25–1 mg/L. Linezolid C<sub>min</sub> impacted platelet levels, with baseline levels influencing the severity of reduction. This PopPK model offers valuable insights for optimizing linezolid dosing, considering immunosuppressant therapy and thrombocytopenia risk, and serves as a reference for dose selection in post-surgical LTRs.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Scotcher PhD, Avijit Ghosh PhD, Aleksandra Galetin PhD, Amin Rostami-Hodjegan PhD, FCP
<p>The global population is becoming older, and prevalence of multimorbidity and polypharmacy is increasing.<span><sup>1</sup></span> Most research into pharmacokinetic drug–drug interactions (DDIs) has focused on pairwise interactions between drugs and corresponding metabolites, while the impact of co-administration of multiple drugs has received less attention. More recently, the differences in DDI risk between population sub-groups has received increased attention (e.g., in pediatrics vs adults<span><sup>2, 3</sup></span>). It is well recognized that simultaneous inhibition of multiple different pathways of a drug's elimination can lead to much larger increases in drug exposure compared with inhibition of only the major pathway.<span><sup>4</sup></span> The potential magnitude of DDI in this scenario is linked with the fraction metabolized (fm) of each pathway for the object (also known as “victim”) drug<span><sup>5, 6</sup></span> (Equation 1). In contrast, there is a common misconception about the cumulative effects of multiple precipitant (also known as “perpetrator”) drugs that competitively inhibit the same enzyme. It is often mistakenly perceived that cumulative effects of multiple weak inhibitors are likely to lead to a strong DDI. This misconception arises despite the true relationship being governed by a simple concentration–response relationship, with a well-known theoretical basis (Equations 1 and 2),<span><sup>4, 5</sup></span> analogous to the dose dependence of competitive inhibition. Surprisingly, there is very limited research on multiple inhibitors and their implications on clinical endpoints such as the ratio of area under the curve of the plasma concentration–time profiles (AUCR) in the interaction phase relative to the control,<span><sup>7</sup></span> possibly contributing to such misunderstandings about polypharmacy–DDI risks.</p><p>The ICH M12 Drug Interaction Studies Guidance defines weak, moderate, and strong CYP inhibitors as causing ≥1.25- to <2-fold, ≥2- to <5-fold, and ≥5-fold increase in AUC of a sensitive index CYP substrate.<span><sup>8</sup></span> Assuming a sensitive CYP substrate will have fm ∼ 1, the corresponding [I]/Ki for weak, moderate, and strong CYP inhibitors will be ≥0.25 to <1, ≥1 to <4, and ≥4, respectively. Hence a 4-fold difference in [I]/Ki (after correction for exposure) may be a useful approach to benchmark between weak, moderate, and strong inhibitors.</p><p>When fm = 1 the relationship between ∑[I]/Ki and AUCR is linear (AUCR = ∑[I]/Ki + 1, Figure 1a). However, this relationship is not proportional due to the non-zero intercept.<span><sup>5, 6, 9</sup></span> Particularly at low ∑[I]/Ki values (e.g., from multiple weak inhibitors), the relative change in AUCR will be much lower than the relative change in [I]/Ki (Figure 1b). In other words, to see a change in AUCR from 1.25 to 2 for a weak inhibitor, the co-administration of three additional inhibitors with equivalent [I]/Ki wou
{"title":"Addressing a Common Misconception of Simple Additive and Cumulative Drug Inhibition: Multiple Weak Inhibitors of Common Metabolic Pathway Do Not Pose a Strong Interaction Risk!","authors":"Daniel Scotcher PhD, Avijit Ghosh PhD, Aleksandra Galetin PhD, Amin Rostami-Hodjegan PhD, FCP","doi":"10.1002/jcph.70154","DOIUrl":"10.1002/jcph.70154","url":null,"abstract":"<p>The global population is becoming older, and prevalence of multimorbidity and polypharmacy is increasing.<span><sup>1</sup></span> Most research into pharmacokinetic drug–drug interactions (DDIs) has focused on pairwise interactions between drugs and corresponding metabolites, while the impact of co-administration of multiple drugs has received less attention. More recently, the differences in DDI risk between population sub-groups has received increased attention (e.g., in pediatrics vs adults<span><sup>2, 3</sup></span>). It is well recognized that simultaneous inhibition of multiple different pathways of a drug's elimination can lead to much larger increases in drug exposure compared with inhibition of only the major pathway.<span><sup>4</sup></span> The potential magnitude of DDI in this scenario is linked with the fraction metabolized (fm) of each pathway for the object (also known as “victim”) drug<span><sup>5, 6</sup></span> (Equation 1). In contrast, there is a common misconception about the cumulative effects of multiple precipitant (also known as “perpetrator”) drugs that competitively inhibit the same enzyme. It is often mistakenly perceived that cumulative effects of multiple weak inhibitors are likely to lead to a strong DDI. This misconception arises despite the true relationship being governed by a simple concentration–response relationship, with a well-known theoretical basis (Equations 1 and 2),<span><sup>4, 5</sup></span> analogous to the dose dependence of competitive inhibition. Surprisingly, there is very limited research on multiple inhibitors and their implications on clinical endpoints such as the ratio of area under the curve of the plasma concentration–time profiles (AUCR) in the interaction phase relative to the control,<span><sup>7</sup></span> possibly contributing to such misunderstandings about polypharmacy–DDI risks.</p><p>The ICH M12 Drug Interaction Studies Guidance defines weak, moderate, and strong CYP inhibitors as causing ≥1.25- to <2-fold, ≥2- to <5-fold, and ≥5-fold increase in AUC of a sensitive index CYP substrate.<span><sup>8</sup></span> Assuming a sensitive CYP substrate will have fm ∼ 1, the corresponding [I]/Ki for weak, moderate, and strong CYP inhibitors will be ≥0.25 to <1, ≥1 to <4, and ≥4, respectively. Hence a 4-fold difference in [I]/Ki (after correction for exposure) may be a useful approach to benchmark between weak, moderate, and strong inhibitors.</p><p>When fm = 1 the relationship between ∑[I]/Ki and AUCR is linear (AUCR = ∑[I]/Ki + 1, Figure 1a). However, this relationship is not proportional due to the non-zero intercept.<span><sup>5, 6, 9</sup></span> Particularly at low ∑[I]/Ki values (e.g., from multiple weak inhibitors), the relative change in AUCR will be much lower than the relative change in [I]/Ki (Figure 1b). In other words, to see a change in AUCR from 1.25 to 2 for a weak inhibitor, the co-administration of three additional inhibitors with equivalent [I]/Ki wou","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12859390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146093915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Obstetric pharmacology is undergoing a long-awaited evolution. Historically, pregnancy was treated as a “confounding condition,” leading to systematic exclusion from drug development programs and evaluation. Increasingly however, the field is shifting toward a data-generating discipline that recognizes pregnancy and lactation as biologically dynamic pharmacologic states. Pregnancy is not dichotomous: physiologic and placental changes evolve across gestation, vary by underlying disease and maternal phenotype, and then resolve variably in the postpartum period. These time-varying processes mean that a single, fixed “pregnancy dose” is often an oversimplification.</p><p>Several developments make this moment feel like a true “infection point” rather than incremental progress. First, the therapeutic pipeline now includes agents with pregnancy-specific value propositions (e.g., FcRn inhibitors for antibody-mediated disease) as well as programs explicitly designed around fetal or neonatal benefit (e.g., maternal immunization). Second, quantitative tools for characterizing maternal, placental, fetal, and lactational exposures have matured, most notably physiologically based pharmacokinetic (PBPK) modeling and population pharmacokinetics. Third, regulatory expectations have become more explicit. The Pregnancy and Lactation Labeling Rule (PLLR)<span><sup>1</sup></span> requires narrative risk summaries supported by data, and the International Council on Harmonization (ICH E21) document<span><sup>2</sup></span> provides a contemporary framework for inclusion of pregnant and breastfeeding women in clinical trials.</p><p>This “inflection point” builds on earlier field-shaping work rather than replacing it. The 2021 FDA/M-CERSI workshop on fetal pharmacology and therapeutics,<span><sup>3</sup></span> and scholarly efforts that followed, helped crystallize the conceptual and methodological agenda for the field. What is changing now is the convergence of pipeline, quantitative methods, and policy guidance, narrowing the gap between what is technically feasible and what is operationally expected. A peer-reviewed supplement in the <i>Journal of Clinical Pharmacology</i> (2022)<span><sup>4</sup></span> further articulates the regulatory science and methodologic foundations for obstetric pharmacology.</p><p>At the same time, realism is essential. Advances in pediatric therapeutics were driven not only by scientific consensus but also by legislation that created incentives and obligations, most notably the Best Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA). Obstetric pharmacology faces an analogous “therapeutic orphan” problem. In the absence of policy levers that reward or require evidence generation, pregnancy data continue to emerge late, often after widespread real-world use has already occurred.</p><p>Against this backdrop, the central challenge for obstetric pharmacology is not simply whether to “include pregnancy,” but
产科药理学正在经历一场期待已久的演变。从历史上看,怀孕被视为一种“混杂情况”,导致系统地排除在药物开发计划和评估之外。然而,该领域越来越多地转向数据生成学科,将怀孕和哺乳视为生物动态药理学状态。妊娠不是两分法:生理和胎盘的变化在整个妊娠期间演变,因潜在疾病和母体表型而异,然后在产后阶段不同地解决。这些随时间变化的过程意味着,单一的、固定的“怀孕剂量”往往是一种过度简化。一些发展让这一刻感觉像是一个真正的“感染点”,而不是渐进的进展。首先,治疗管线现在包括具有妊娠特异性价值主张的药物(例如,用于抗体介导疾病的FcRn抑制剂)以及明确围绕胎儿或新生儿益处设计的项目(例如,孕产妇免疫)。其次,表征母体、胎盘、胎儿和哺乳期暴露的定量工具已经成熟,最值得注意的是基于生理的药代动力学(PBPK)模型和群体药代动力学。第三,监管预期变得更加明确。妊娠和哺乳标签规则(PLLR)1要求有数据支持的叙述性风险摘要,国际协调理事会(ICH E21)文件2提供了将孕妇和哺乳期妇女纳入临床试验的当代框架。这个“拐点”是建立在早期的田野塑造工作之上的,而不是取代它。2021年FDA/M-CERSI关于胎儿药理学和治疗学的研讨会,以及随后的学术努力,帮助明确了该领域的概念和方法议程。现在正在发生的变化是管道、定量方法和政策指导的融合,缩小了技术上可行与操作预期之间的差距。《临床药理学杂志》(Journal of Clinical Pharmacology, 2022)的一篇同行评议增刊进一步阐明了产科药理学的监管科学和方法学基础。与此同时,现实主义是必不可少的。儿科治疗的进步不仅受到科学共识的推动,还受到立法的推动,立法创造了激励和义务,最著名的是《儿童最佳药品法案》(BPCA)和《儿科研究公平法案》(PREA)。产科药理学面临着类似的“治疗孤儿”问题。在缺乏奖励或要求证据生成的政策杠杆的情况下,妊娠数据的出现仍然很晚,往往是在实际应用已经广泛之后才出现。在这种背景下,产科药理学面临的核心挑战不仅仅是是否“包括妊娠”,而是如何产生及时、道德和符合目的的决策级证据(表1)。解决这一挑战需要证据架构,包括临床前翻译系统、妊娠适应药物计量方法、实用嵌入式临床试验和可互操作的现实世界注册(表2)。这些组成部分共同定义了使产科药理学的科学能力与监管和临床决策相一致所需的新框架。这种转变最引人注目的表现之一是明确设计用于调节妊娠特异性生物学的治疗方法。当代管道的几个发展例证了这种新兴范式。实现这些管道进展的承诺取决于用于产生和解释妊娠证据的方法框架的平行演变。在未来的十年中,一些发展将重新定义产科药理学:尽管取得了实质性进展,但仍有一些挑战继续限制妊娠证据的产生。如果产科药理学要成为发展计划的常规,科学论证必须与政策机制相结合。在BPCA制定了激励措施和PREA制定了在特定条件下进行儿科评估的要求之后,儿科治疗加速了。类似的产科框架可包括:(i)产科调查计划(OIP)对有生殖潜力的人可能使用的药物的期望;(ii)对及时妊娠和安全性研究的激励措施(例如,监管优先途径、排他性机制或公私合营试验基础设施信贷);(iii)当预先批准纳入不可行时,更明确的授权后承诺触发机制。ICH E21为这种框架提供了伦理和科学框架,但可能需要激励和义务使其系统化而不是零星地采用。前瞻性议程至关重要。 首先,针对怀孕期和哺乳期的特定发展策略应该尽早启动,用于或可能用于育龄妇女的药物,即使主要适应症不是针对怀孕期。早期生殖毒理学、妊娠和哺乳期PBPK以及胎盘移植评估可以降低前瞻性研究的风险,并为在发育过程中怀孕时制定“保留试验”方法提供信息。第二,共享基础设施应被视为公共卫生资产。nichd支持的产科胎儿药理学研究中心和类似的网络可以提供招募能力,标准化的采样窗口,验证的分析,以及跨发起人和适应症的方法连续性国际合作至关重要,这样才能使证据在单一管辖范围之外得到推广。第三,产科药理学应该正式制定双重透明的合同。这包括透明地报告妊娠暴露数据(包括阴性或无效结果),以及在临床咨询和标签中透明地传达不确定性。这对于长效药物尤其重要,因为无意的暴露是可以预测的,而不是例外。最后,产科药理学必须接受精确的原则,不是在理想的意义上,而是作为一种必要。胎龄、胎盘功能、泌乳强度、合并症表型和药物基因组学背景是暴露和反应的决定因素。仅仅将它们视为“需要调整的协变量”,就失去了对母胎双体进行个体化治疗的机会。产科药理学正处于一个拐点,因为管道,定量方法和监管框架正在趋同。FcRn抑制剂、孕产妇免疫平台和长效药物表明,针对妊娠和妊娠相关的创新正在加速(表2)。决定性的约束是执行。这将涉及可互操作的数据系统、特定于怀孕的安全基础设施,以及使证据生成成为默认而不是例外的策略杠杆。最终,评判产科药理学的标准不是它对纳入的论证有多有说服力,而是它提供的证据有多可靠,从而使纳入对孕妇和胎儿都更安全。内容完全是作者的责任,并不一定代表美国国立卫生研究院的官方观点。没有宣布利益冲突。这项工作由美国国立卫生研究院(NIH)的尤尼斯·肯尼迪·施莱佛国家儿童健康与人类发展研究所(NICHD)提供总体支持,奖励号为DP1HD115433。数据共享不适用于本文,因为没有生成或分析新的或重新生成的数据集。这项工作是一个学术评论的基础上的综合,解释,和背景化以前发表的文献和公开可用的信息。
{"title":"Obstetric Pharmacology at an Inflection Point","authors":"Ahizechukwu C. Eke MD, PhD, MPH","doi":"10.1002/jcph.70152","DOIUrl":"10.1002/jcph.70152","url":null,"abstract":"<p>Obstetric pharmacology is undergoing a long-awaited evolution. Historically, pregnancy was treated as a “confounding condition,” leading to systematic exclusion from drug development programs and evaluation. Increasingly however, the field is shifting toward a data-generating discipline that recognizes pregnancy and lactation as biologically dynamic pharmacologic states. Pregnancy is not dichotomous: physiologic and placental changes evolve across gestation, vary by underlying disease and maternal phenotype, and then resolve variably in the postpartum period. These time-varying processes mean that a single, fixed “pregnancy dose” is often an oversimplification.</p><p>Several developments make this moment feel like a true “infection point” rather than incremental progress. First, the therapeutic pipeline now includes agents with pregnancy-specific value propositions (e.g., FcRn inhibitors for antibody-mediated disease) as well as programs explicitly designed around fetal or neonatal benefit (e.g., maternal immunization). Second, quantitative tools for characterizing maternal, placental, fetal, and lactational exposures have matured, most notably physiologically based pharmacokinetic (PBPK) modeling and population pharmacokinetics. Third, regulatory expectations have become more explicit. The Pregnancy and Lactation Labeling Rule (PLLR)<span><sup>1</sup></span> requires narrative risk summaries supported by data, and the International Council on Harmonization (ICH E21) document<span><sup>2</sup></span> provides a contemporary framework for inclusion of pregnant and breastfeeding women in clinical trials.</p><p>This “inflection point” builds on earlier field-shaping work rather than replacing it. The 2021 FDA/M-CERSI workshop on fetal pharmacology and therapeutics,<span><sup>3</sup></span> and scholarly efforts that followed, helped crystallize the conceptual and methodological agenda for the field. What is changing now is the convergence of pipeline, quantitative methods, and policy guidance, narrowing the gap between what is technically feasible and what is operationally expected. A peer-reviewed supplement in the <i>Journal of Clinical Pharmacology</i> (2022)<span><sup>4</sup></span> further articulates the regulatory science and methodologic foundations for obstetric pharmacology.</p><p>At the same time, realism is essential. Advances in pediatric therapeutics were driven not only by scientific consensus but also by legislation that created incentives and obligations, most notably the Best Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA). Obstetric pharmacology faces an analogous “therapeutic orphan” problem. In the absence of policy levers that reward or require evidence generation, pregnancy data continue to emerge late, often after widespread real-world use has already occurred.</p><p>Against this backdrop, the central challenge for obstetric pharmacology is not simply whether to “include pregnancy,” but","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://accp1.onlinelibrary.wiley.com/doi/epdf/10.1002/jcph.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Renad Bin Naheet PharmD, Khalid Al Sulaiman BCCCP, BCNSP, MBA, FCCM, Khuld Aloufi PharmD, Abdullah F. Alharthi PharmD, Mashael AlFaifi PharmD, Lama Nazer PharmD, Mohammad Shawaqfeh PharmD, Abdulmajeed M. Alshehri PharmD, Mohammed Abutaleb PharmD, Abdulaziz Alarifi PhD, Asma A. Alshehri PharmD, Ahmad H. Al-Ani PharmD, Omar Saggaf MD, Ramesh Vishwakarma PhD, Maryam Alharbi PharmD, Delael Alnaser PharmD, Saud Alrashdi PharmD, Abdulaziz A. Jaly PharmD, Farhan Alenezi MD, Moayad Alkhlewi MD, Abdulelah Alanazi MSc, Ohoud Aljuhani PharmD
Valproic acid (VPA) is highly protein-bound, thereby impacting its free fraction and clearance in hypoalbuminemia. There is limited data on VPA use in such patients. Thus, this study evaluates the impact of adjusted VPA concentration (aVPAc) in predicting effectiveness and adverse effects compared to total VPA (tVPA) levels in hypoalbuminemic patients. A retrospective cohort study involved adult patients with seizures or epilepsy between January 1, 2016, and December 31, 2022. The levels of tVPA and aVPA (adjusted for albumin) were compared using receiver operating characteristic curves, and AUC differences were assessed using the DeLong method. Safety endpoints included hepatotoxicity, hyperammonemia, hyponatremia, and thrombocytopenia, while effectiveness endpoints were seizure occurrence, status epilepticus, and the use of additional antiepileptic medications during hospitalization. Of the 1621 screened patients, 71 with hypoalbuminemia received VPA. An aVPAc threshold of 154.19 mg/dL demonstrated higher sensitivity (86%) but lower specificity (47%) for predicting hepatotoxicity compared to a tVPA threshold of 67.53 mg/dL (sensitivity: 71%, specificity: 72%). Although aVPAc yielded a comparable negative predictive value (96% vs 95%), tVPA showed superior positive predictive value (25% vs 18%) and a higher Youden index (0.43 vs 0.33), indicating better overall discriminatory performance; however, these findings did not achieve statistical significance. In contrast, an aVPAc threshold of 188 mg/dL showed a sensitivity of 100% and a specificity of 82% for predicting hyperammonemia, which is superior to the tVPA threshold of 74.32 mg/dL that has a sensitivity of 40% and a specificity of 88%. The aVPAc also achieved a higher Youden index of 0.82 compared to 0.28 for tVPA. Adjusted VPA concentrations showed greater sensitivity than tVPA in predicting hepatotoxicity and hyperammonemia, suggesting potential utility for ruling out these adverse effects in hypoalbuminemic patients. Further research with a larger sample size is needed to validate these findings.
{"title":"Clinical Impact of Adjusted Valproic Acid Level in Patients with Hypoalbuminemia: A Single-Center Cohort Study","authors":"Renad Bin Naheet PharmD, Khalid Al Sulaiman BCCCP, BCNSP, MBA, FCCM, Khuld Aloufi PharmD, Abdullah F. Alharthi PharmD, Mashael AlFaifi PharmD, Lama Nazer PharmD, Mohammad Shawaqfeh PharmD, Abdulmajeed M. Alshehri PharmD, Mohammed Abutaleb PharmD, Abdulaziz Alarifi PhD, Asma A. Alshehri PharmD, Ahmad H. Al-Ani PharmD, Omar Saggaf MD, Ramesh Vishwakarma PhD, Maryam Alharbi PharmD, Delael Alnaser PharmD, Saud Alrashdi PharmD, Abdulaziz A. Jaly PharmD, Farhan Alenezi MD, Moayad Alkhlewi MD, Abdulelah Alanazi MSc, Ohoud Aljuhani PharmD","doi":"10.1002/jcph.70139","DOIUrl":"10.1002/jcph.70139","url":null,"abstract":"<p>Valproic acid (VPA) is highly protein-bound, thereby impacting its free fraction and clearance in hypoalbuminemia. There is limited data on VPA use in such patients. Thus, this study evaluates the impact of adjusted VPA concentration (aVPAc) in predicting effectiveness and adverse effects compared to total VPA (tVPA) levels in hypoalbuminemic patients. A retrospective cohort study involved adult patients with seizures or epilepsy between January 1, 2016, and December 31, 2022. The levels of tVPA and aVPA (adjusted for albumin) were compared using receiver operating characteristic curves, and AUC differences were assessed using the DeLong method. Safety endpoints included hepatotoxicity, hyperammonemia, hyponatremia, and thrombocytopenia, while effectiveness endpoints were seizure occurrence, status epilepticus, and the use of additional antiepileptic medications during hospitalization. Of the 1621 screened patients, 71 with hypoalbuminemia received VPA. An aVPAc threshold of 154.19 mg/dL demonstrated higher sensitivity (86%) but lower specificity (47%) for predicting hepatotoxicity compared to a tVPA threshold of 67.53 mg/dL (sensitivity: 71%, specificity: 72%). Although aVPAc yielded a comparable negative predictive value (96% vs 95%), tVPA showed superior positive predictive value (25% vs 18%) and a higher Youden index (0.43 vs 0.33), indicating better overall discriminatory performance; however, these findings did not achieve statistical significance. In contrast, an aVPAc threshold of 188 mg/dL showed a sensitivity of 100% and a specificity of 82% for predicting hyperammonemia, which is superior to the tVPA threshold of 74.32 mg/dL that has a sensitivity of 40% and a specificity of 88%. The aVPAc also achieved a higher Youden index of 0.82 compared to 0.28 for tVPA. Adjusted VPA concentrations showed greater sensitivity than tVPA in predicting hepatotoxicity and hyperammonemia, suggesting potential utility for ruling out these adverse effects in hypoalbuminemic patients. Further research with a larger sample size is needed to validate these findings.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146031251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lan Gao PhD, Hongqi Xue PhD, Borje Darpo MD, PhD, Kimberly Ingalls MD, Diksha Kaushik PhD, Neil Smith PharmD, Ronald Kong PhD, Lee Golden MD
Sepiapterin and its major metabolite 6R-L-erythro-5,6,7,8-tetrahydrobiopterin (BH4) bind to distinct variants of phenylalanine hydroxylase (PAH), which converts excess phenylalanine to tyrosine, thereby stabilizing, enhancing, and prolonging PAH activity. Sepiapterin was recently approved in Europe and the USA for the treatment of hyperphenylalaninemia patients with phenylketonuria, an inherent metabolic disease caused by PAH deficiency. A thorough QT study of sepiapterin in healthy volunteers at therapeutic (60 mg/kg) and supratherapeutic (120 mg/kg) doses was conducted to assess potential cardiovascular risks. Thirty-two participants were randomized into one of 12 sequences and received single doses of sepiapterin (60 or 120 mg/kg), moxifloxacin 400 mg, or placebo in separate periods. Sepiapterin had no effect on heart rate or cardiac conduction (PR/QRS interval). Saturable sepiapterin absorption was observed, which resulted in less than dose-proportional increase of sepiapterin and BH4 and limited the maximum plasma concentrations clinically achievable. Using concentration-QT analysis, the placebo-corrected change from baseline in QT interval corrected using Fridericia's formula (ΔΔQTcF) was −2.11 (90% CI: −3.44, −0.79) ms at geometric mean baseline-corrected BH4 Cmax (728 ng/mL) and −1.9 (−3.25, −0.56) ms at sepiapterin Cmax (2.08 ng/mL) at the supratherapeutic dose of 120 mg/kg. An effect on ΔΔQTcF exceeding 10 ms was excluded within the observed concentration range of baseline-corrected BH4 up to 1088 ng/mL and sepiapterin up to 5.77 ng/mL. The consistency of results from this study and the previous concentration-QTc analysis based on pooled data from multiple clinical studies demonstrated the reliability of using concentration-QTc for assessing cardiovascular risks in early clinical development.
{"title":"No QTcF Prolongation with Sepiapterin: Results From a Thorough QT Study in Healthy Subjects at Therapeutic and Supratherapeutic Doses","authors":"Lan Gao PhD, Hongqi Xue PhD, Borje Darpo MD, PhD, Kimberly Ingalls MD, Diksha Kaushik PhD, Neil Smith PharmD, Ronald Kong PhD, Lee Golden MD","doi":"10.1002/jcph.70149","DOIUrl":"10.1002/jcph.70149","url":null,"abstract":"<p>Sepiapterin and its major metabolite <i>6R</i>-L-erythro-5,6,7,8-tetrahydrobiopterin (BH<sub>4</sub>) bind to distinct variants of phenylalanine hydroxylase (PAH), which converts excess phenylalanine to tyrosine, thereby stabilizing, enhancing, and prolonging PAH activity. Sepiapterin was recently approved in Europe and the USA for the treatment of hyperphenylalaninemia patients with phenylketonuria, an inherent metabolic disease caused by PAH deficiency. A thorough QT study of sepiapterin in healthy volunteers at therapeutic (60 mg/kg) and supratherapeutic (120 mg/kg) doses was conducted to assess potential cardiovascular risks. Thirty-two participants were randomized into one of 12 sequences and received single doses of sepiapterin (60 or 120 mg/kg), moxifloxacin 400 mg, or placebo in separate periods. Sepiapterin had no effect on heart rate or cardiac conduction (PR/QRS interval). Saturable sepiapterin absorption was observed, which resulted in less than dose-proportional increase of sepiapterin and BH<sub>4</sub> and limited the maximum plasma concentrations clinically achievable. Using concentration-QT analysis, the placebo-corrected change from baseline in QT interval corrected using Fridericia's formula (ΔΔQTcF) was −2.11 (90% CI: −3.44, −0.79) ms at geometric mean baseline-corrected BH<sub>4</sub> C<sub>max</sub> (728 ng/mL) and −1.9 (−3.25, −0.56) ms at sepiapterin C<sub>max</sub> (2.08 ng/mL) at the supratherapeutic dose of 120 mg/kg. An effect on ΔΔQTcF exceeding 10 ms was excluded within the observed concentration range of baseline-corrected BH<sub>4</sub> up to 1088 ng/mL and sepiapterin up to 5.77 ng/mL. The consistency of results from this study and the previous concentration-QTc analysis based on pooled data from multiple clinical studies demonstrated the reliability of using concentration-QTc for assessing cardiovascular risks in early clinical development.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12801174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Dear Editor,</p><p>We read with great interest the study by Wagstaff et al,<span><sup>1</sup></span> which evaluated a decision support tool (DST) based on a population pharmacokinetic (popPK) model to individualize tacrolimus dosing in pediatric heart transplant recipients. This innovative work addresses a key challenge in pediatric transplantation—the safe and timely attainment of therapeutic immunosuppression in a population marked by substantial pharmacokinetic variability. While the study represents progress toward precision dosing, several methodological and interpretive aspects merit further consideration for clinical translation.</p><p>The prospective evaluation of a Bayesian DST marks an important translational step beyond model development. However, the single-center design and small sample size (n = 15) limit generalizability, given the ethnically homogeneous cohort and limited genetic characterization. The use of a historical rather than concurrent control introduces potential bias from temporal changes in clinical practice.<span><sup>2</sup></span> Although restricting analysis to first-time pediatric transplant recipients ensures population uniformity, the post hoc exclusion of patients receiving continuous renal replacement therapy (CRRT) highlights the need for clearer inclusion criteria to ensure consistent applicability.</p><p>The reported 3-day reduction in time to stable therapeutic tacrolimus concentrations (<i>P</i> = .03) is clinically meaningful. Nonetheless, the small sample and absence of randomization raise concerns about statistical stability and confounding factors such as age and concomitant antifungal use. Reporting confidence intervals for mean differences would strengthen interpretation by defining precision around estimates. While the DST's fidelity to NONMEM modeling was validated in silico,<span><sup>3</sup></span> clinical performance depends on appropriate covariate weighting and sampling frequency areas underexplored in the manuscript.</p><p>Reliance on retrospective data for model calibration is reasonable, yet the dependence on creatinine clearance as a surrogate for tacrolimus disposition warrants further justification. As the authors note, creatinine clearance likely reflects broader physiological influences, such as hepatic perfusion, rather than renal elimination, underscoring the need for refinement of structural assumptions. Moreover, the absence of CYP3A5 genotyping during DST calibration limits predictive robustness. Incorporating pharmacogenomic data could enhance parameter individualization and reduce misdosing in expressor phenotypes.<span><sup>4</sup></span></p><p>Clinically, the DST offers a promising framework for model-informed precision dosing (MIPD) in pediatric transplantation. The observed reduction in time to therapeutic range may translate to shorter hospital stays, fewer blood draws, and improved graft outcomes. However, implementation outside the electronic medical record (EMR) co
尊敬的编辑,我们怀着极大的兴趣阅读了Wagstaff等人1的研究,该研究评估了基于群体药代动力学(popPK)模型的决策支持工具(DST),用于儿童心脏移植受者个体化他克莫司剂量。这项创新的工作解决了儿科移植的一个关键挑战——在一个以大量药代动力学变异性为特征的人群中安全、及时地实现治疗性免疫抑制。虽然这项研究代表了精确给药的进展,但一些方法学和解释方面值得进一步考虑临床翻译。贝叶斯DST的前瞻性评估标志着模型开发之外的重要转化步骤。然而,单中心设计和小样本量(n = 15)限制了通用性,考虑到种族同质队列和有限的遗传特征。使用历史对照而非同期对照会在临床实践中引入时间变化的潜在偏差虽然限制首次儿童移植受者的分析确保了人群的一致性,但对接受持续肾替代治疗(CRRT)的患者的临时排除强调了需要更明确的纳入标准,以确保一致性的适用性。报告的他克莫司治疗浓度稳定所需时间缩短3天(P = .03)具有临床意义。尽管如此,小样本和缺乏随机化引起了对统计稳定性和混淆因素(如年龄和伴随的抗真菌药物使用)的担忧。报告均值差异的置信区间将通过定义估计值的精度来加强解释。虽然DST对NONMEM模型的保真度在计算机上得到了验证,但临床性能取决于适当的协变量加权和手稿中未充分探讨的采样频率区域。依赖回顾性数据进行模型校准是合理的,但依赖肌酐清除率作为他克莫司处置的替代指标需要进一步的证明。正如作者所指出的,肌酐清除率可能反映了更广泛的生理影响,如肝脏灌注,而不是肾脏消除,强调了对结构假设的改进的需要。此外,在DST校准期间缺乏CYP3A5基因分型限制了预测的稳健性。结合药物基因组学数据可以增强参数个体化,减少表达表型的误给药。在临床上,DST为儿童移植中模型知情精确给药(MIPD)提供了一个有前景的框架。观察到的缩短到治疗范围的时间可能转化为更短的住院时间、更少的抽血和更好的移植物结果。然而,电子病历(EMR)之外的实现限制了可伸缩性。无缝的EMR集成、自动数据捕获和优化的接口是广泛采用的先决条件。对于动态情况的患者,如接受CRRT或相互作用药物治疗的患者,仍需谨慎,有待进一步验证。Wagstaff等人的工作代表了他克莫司在儿童心脏移植中可操作的MIPD的重大进展。未来的研究应寻求多中心验证,采用不同的队列,整合药物基因组学数据,并评估长期结果,如排斥反应和肾毒性。将定量建模与临床可用性相结合,可以使MIPD从研究发展到儿科移植免疫抑制的标准护理。这项工作没有收到外部资金。作者声明无利益冲突。生成式人工智能工具(ChatGPT-5和Paperpal)仅用于语法、结构和风格改进。所有的解释、临床观点和评论都是由作者独立开发和验证的。卡什亚普公主参与了手稿的构思、监督和最终审查。Manoj Kumar对验证、方法批判和编辑做出了贡献。Ramenani Hari Babu负责起草和临床解释,Mahesh Kumar Gupta负责文献综述、数据验证和初步稿件准备。所有作者在提交之前都审查并批准了最终版本。没有产生或分析新的数据。
{"title":"Comment on “Model Informed Precision Dosing of Tacrolimus in Children Following Heart Transplant”","authors":"Princy Kashyap PhD, Manoj Kumar PhD, Ramenani Hari Babu PhD, Mahesh Kumar Gupta PhD","doi":"10.1002/jcph.70140","DOIUrl":"10.1002/jcph.70140","url":null,"abstract":"<p>Dear Editor,</p><p>We read with great interest the study by Wagstaff et al,<span><sup>1</sup></span> which evaluated a decision support tool (DST) based on a population pharmacokinetic (popPK) model to individualize tacrolimus dosing in pediatric heart transplant recipients. This innovative work addresses a key challenge in pediatric transplantation—the safe and timely attainment of therapeutic immunosuppression in a population marked by substantial pharmacokinetic variability. While the study represents progress toward precision dosing, several methodological and interpretive aspects merit further consideration for clinical translation.</p><p>The prospective evaluation of a Bayesian DST marks an important translational step beyond model development. However, the single-center design and small sample size (n = 15) limit generalizability, given the ethnically homogeneous cohort and limited genetic characterization. The use of a historical rather than concurrent control introduces potential bias from temporal changes in clinical practice.<span><sup>2</sup></span> Although restricting analysis to first-time pediatric transplant recipients ensures population uniformity, the post hoc exclusion of patients receiving continuous renal replacement therapy (CRRT) highlights the need for clearer inclusion criteria to ensure consistent applicability.</p><p>The reported 3-day reduction in time to stable therapeutic tacrolimus concentrations (<i>P</i> = .03) is clinically meaningful. Nonetheless, the small sample and absence of randomization raise concerns about statistical stability and confounding factors such as age and concomitant antifungal use. Reporting confidence intervals for mean differences would strengthen interpretation by defining precision around estimates. While the DST's fidelity to NONMEM modeling was validated in silico,<span><sup>3</sup></span> clinical performance depends on appropriate covariate weighting and sampling frequency areas underexplored in the manuscript.</p><p>Reliance on retrospective data for model calibration is reasonable, yet the dependence on creatinine clearance as a surrogate for tacrolimus disposition warrants further justification. As the authors note, creatinine clearance likely reflects broader physiological influences, such as hepatic perfusion, rather than renal elimination, underscoring the need for refinement of structural assumptions. Moreover, the absence of CYP3A5 genotyping during DST calibration limits predictive robustness. Incorporating pharmacogenomic data could enhance parameter individualization and reduce misdosing in expressor phenotypes.<span><sup>4</sup></span></p><p>Clinically, the DST offers a promising framework for model-informed precision dosing (MIPD) in pediatric transplantation. The observed reduction in time to therapeutic range may translate to shorter hospital stays, fewer blood draws, and improved graft outcomes. However, implementation outside the electronic medical record (EMR) co","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://accp1.onlinelibrary.wiley.com/doi/epdf/10.1002/jcph.70140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Disparities in the performance of various kidney function equations in predicting aminoglycoside clearance (CL) have been identified. However, data specific to the Thai population remains limited. This study aimed to evaluate the performance of kidney function equations in estimating aminoglycoside CL in Thai patients. Data were retrospectively collected from hospitalized Thai patients who received amikacin or gentamicin over a 10-year period. Population pharmacokinetic (PK) analysis was performed using a nonlinear mixed-effects modeling approach. The association between aminoglycoside CL and kidney function, estimated by various equations, was ranked based on the magnitude of change in the Akaike Information Criterion (AIC) relative to the base model. A total of 138 adult Thai patients treated with either gentamicin (66%) or amikacin (34%) were enrolled. The non-body surface area (BSA)-indexed estimated glomerular filtration rate (eGFR) equations showed a stronger association with aminoglycoside CL compared to the 1.73 m2 BSA-indexed equations. The non-BSA-indexed 2021 Chronic Kidney Disease Epidemiology Collaboration eGFR (2021 CKD-EPI eGFRcr) equation demonstrated the highest association with aminoglycoside CL. The estimated volume of distribution (V) and CL from the final model were 21.91 L and 2.62 L/h, respectively. Among the Thai population, the non-BSA-indexed 2021 CKD-EPI eGFRcr equation of the non-race demonstrated the highest performance in estimating aminoglycoside CL. Further studies are warranted to confirm these findings with other renally eliminated drugs.
{"title":"Performance of Estimated Kidney Function Equations for Predicting Aminoglycosides Clearance in Thai Population","authors":"Sirima Sitaruno PharmD, BCP, Warunsuda Sripakdee BPharm, BCP, Orawan Sae-lim PharmD, BCP, Dissaya Watthanapaisal PharmD, BCP, Nuntapong Boonrit PharmD, BCP, Kasemsiri Chandarajoti BPharm, PhD, Rungsun Bhurayanontachai MD, Sutthiporn Pattharachayakul PharmD, BCP","doi":"10.1002/jcph.70150","DOIUrl":"10.1002/jcph.70150","url":null,"abstract":"<p>Disparities in the performance of various kidney function equations in predicting aminoglycoside clearance (CL) have been identified. However, data specific to the Thai population remains limited. This study aimed to evaluate the performance of kidney function equations in estimating aminoglycoside CL in Thai patients. Data were retrospectively collected from hospitalized Thai patients who received amikacin or gentamicin over a 10-year period. Population pharmacokinetic (PK) analysis was performed using a nonlinear mixed-effects modeling approach. The association between aminoglycoside CL and kidney function, estimated by various equations, was ranked based on the magnitude of change in the Akaike Information Criterion (AIC) relative to the base model. A total of 138 adult Thai patients treated with either gentamicin (66%) or amikacin (34%) were enrolled. The non-body surface area (BSA)-indexed estimated glomerular filtration rate (eGFR) equations showed a stronger association with aminoglycoside CL compared to the 1.73 m<sup>2</sup> BSA-indexed equations. The non-BSA-indexed 2021 Chronic Kidney Disease Epidemiology Collaboration eGFR (2021 CKD-EPI eGFRcr) equation demonstrated the highest association with aminoglycoside CL. The estimated volume of distribution (V) and CL from the final model were 21.91 L and 2.62 L/h, respectively. Among the Thai population, the non-BSA-indexed 2021 CKD-EPI eGFRcr equation of the non-race demonstrated the highest performance in estimating aminoglycoside CL. Further studies are warranted to confirm these findings with other renally eliminated drugs.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simon Erridge MBBS, BSc, Evonne Clarke MSc, IPresc, Katy McLachlan MPharm, IPresc, Ross Coomber BSc, FRCS, Sushil Beri MD, MRCPCH, Shaheen Khan MSc, MRCP, Mark W. Weatherall PhD, FRCP, Michael W. Platt MA, FRCA, FFPM, RCA, James J. Rucker PhD, MRCPsych, Pedro A. M. Mediano MSc, PhD, Mikael H. Sodergren PhD, FRCS
There is a paucity of high-quality evidence on the clinical efficacy of cannabis-based medicinal products (CBMPs). The objective of this study was to perform trajectory k-means clustering of health-related quality of life (HRQoL) outcomes in patients prescribed CBMPs to identify distinct response patterns and baseline predictors of treatment outcomes over 24 months. A cohort study of patients enrolled in the UK Medical Cannabis Registry with any qualifying indication was performed. Participants completed patient-reported outcome measures including EuroQol 5-Dimension 5-Level (EQ-5D-5L), Generalized Anxiety Disorder-7 (GAD-7), and Single-Item Sleep Quality Scale (SQS), at baseline, 1, 3, 6, 12, 18, and 24 months. Longitudinal k-means clustering was performed on EQ-5D-5L index values where the optimal number of clusters was selected via the gap statistic. Univariable and multivariable logistic regression analyses identified predictors of cluster membership. The 8945 patients were included in the analysis, from which 10 distinct trajectory clusters were identified, with eight demonstrating HRQoL improvements representing 77.72% of the cohort (n = 6952). Over 70% of participants reported improved EQ-5D-5L index values at each timepoint, whilst 54.21% (n = 4849) and 44.07% (n = 3942) achieved clinically significant improvements in GAD-7 and SQS at 24 months, respectively. Adverse events were reported by 13.65% (n = 1221) of patients, predominantly rated as mild (n = 4732; 42.31%) or moderate (n = 4860; 43.46%). Baseline patient characteristics, particularly treatment indication, severe anxiety, poor sleep quality, female sex, and cannabis-naïve status, were stronger predictors of favorable treatment response than product-specific factors.
{"title":"Clinical Outcomes and Patient Profiles in the UK Medical Cannabis Registry: A k-Means Clustering Analysis","authors":"Simon Erridge MBBS, BSc, Evonne Clarke MSc, IPresc, Katy McLachlan MPharm, IPresc, Ross Coomber BSc, FRCS, Sushil Beri MD, MRCPCH, Shaheen Khan MSc, MRCP, Mark W. Weatherall PhD, FRCP, Michael W. Platt MA, FRCA, FFPM, RCA, James J. Rucker PhD, MRCPsych, Pedro A. M. Mediano MSc, PhD, Mikael H. Sodergren PhD, FRCS","doi":"10.1002/jcph.70151","DOIUrl":"10.1002/jcph.70151","url":null,"abstract":"<p>There is a paucity of high-quality evidence on the clinical efficacy of cannabis-based medicinal products (CBMPs). The objective of this study was to perform trajectory k-means clustering of health-related quality of life (HRQoL) outcomes in patients prescribed CBMPs to identify distinct response patterns and baseline predictors of treatment outcomes over 24 months. A cohort study of patients enrolled in the UK Medical Cannabis Registry with any qualifying indication was performed. Participants completed patient-reported outcome measures including EuroQol 5-Dimension 5-Level (EQ-5D-5L), Generalized Anxiety Disorder-7 (GAD-7), and Single-Item Sleep Quality Scale (SQS), at baseline, 1, 3, 6, 12, 18, and 24 months. Longitudinal k-means clustering was performed on EQ-5D-5L index values where the optimal number of clusters was selected via the gap statistic. Univariable and multivariable logistic regression analyses identified predictors of cluster membership. The 8945 patients were included in the analysis, from which 10 distinct trajectory clusters were identified, with eight demonstrating HRQoL improvements representing 77.72% of the cohort (n = 6952). Over 70% of participants reported improved EQ-5D-5L index values at each timepoint, whilst 54.21% (n = 4849) and 44.07% (n = 3942) achieved clinically significant improvements in GAD-7 and SQS at 24 months, respectively. Adverse events were reported by 13.65% (n = 1221) of patients, predominantly rated as mild (n = 4732; 42.31%) or moderate (n = 4860; 43.46%). Baseline patient characteristics, particularly treatment indication, severe anxiety, poor sleep quality, female sex, and cannabis-naïve status, were stronger predictors of favorable treatment response than product-specific factors.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145935685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mai Tarek BSc (Pharmacy), Ahmed A. Ali PhD, Reda Biomy PhD, Khaled Abdelkawy PhD, Eman El-Khateeb PhD
Liver cirrhosis can alter drug pharmacokinetics, often requiring dose adjustments. Physiologically based pharmacokinetic (PBPK) modeling aids in predicting these pharmacokinetic changes in cirrhosis patients. This study developed and validated PBPK models for multiple antihypertensive drugs to predict dosing across varying severities of cirrhosis. Models were initially validated in healthy volunteers, then adjusted to incorporate cirrhosis-specific pathophysiological changes. Predicted results (area under the curve [AUC] and plasma maximum concentration [Cmax]) showed good agreement with clinical data (within 2-fold). The models were further used to simulate untested cirrhotic populations and to estimate unbound plasma AUC across different disease stages, and the results were compared with the healthy population. The model predicted that the healthy doses of nifedipine 20 mg three times daily (TID), verapamil 80 mg TID, nebivolol 10 mg once daily, and diltiazem 60 mg TID should be adjusted to 44%, 41%, 49.8%, and 51% of these doses, respectively, in the mild cirrhosis population. For moderate cirrhosis, the predicted reductions were to 21%, 25%, 29.8%, and 39%, respectively. In severe cirrhosis, greater reductions to 9.9%, 19%, 12.3%, and 26%, respectively, were necessary to achieve the same unbound drug exposures as in healthy subjects. Among the studied drugs, nifedipine was the most affected and diltiazem was the least affected by cirrhosis, highlighting variability in hepatic impact across antihypertensive drugs. In the absence of dedicated clinical trials in cirrhosis for these drugs, validated PBPK models offer evidence-based insights to support clinicians in evaluating antihypertensive dosing options.
{"title":"Using Physiologically Based Pharmacokinetic Modeling and Simulations to Predict Antihypertensive Drug Doses in Cirrhotic Patients","authors":"Mai Tarek BSc (Pharmacy), Ahmed A. Ali PhD, Reda Biomy PhD, Khaled Abdelkawy PhD, Eman El-Khateeb PhD","doi":"10.1002/jcph.70148","DOIUrl":"10.1002/jcph.70148","url":null,"abstract":"<p>Liver cirrhosis can alter drug pharmacokinetics, often requiring dose adjustments. Physiologically based pharmacokinetic (PBPK) modeling aids in predicting these pharmacokinetic changes in cirrhosis patients. This study developed and validated PBPK models for multiple antihypertensive drugs to predict dosing across varying severities of cirrhosis. Models were initially validated in healthy volunteers, then adjusted to incorporate cirrhosis-specific pathophysiological changes. Predicted results (area under the curve [AUC] and plasma maximum concentration [Cmax]) showed good agreement with clinical data (within 2-fold). The models were further used to simulate untested cirrhotic populations and to estimate unbound plasma AUC across different disease stages, and the results were compared with the healthy population. The model predicted that the healthy doses of nifedipine 20 mg three times daily (TID), verapamil 80 mg TID, nebivolol 10 mg once daily, and diltiazem 60 mg TID should be adjusted to 44%, 41%, 49.8%, and 51% of these doses, respectively, in the mild cirrhosis population. For moderate cirrhosis, the predicted reductions were to 21%, 25%, 29.8%, and 39%, respectively. In severe cirrhosis, greater reductions to 9.9%, 19%, 12.3%, and 26%, respectively, were necessary to achieve the same unbound drug exposures as in healthy subjects. Among the studied drugs, nifedipine was the most affected and diltiazem was the least affected by cirrhosis, highlighting variability in hepatic impact across antihypertensive drugs. In the absence of dedicated clinical trials in cirrhosis for these drugs, validated PBPK models offer evidence-based insights to support clinicians in evaluating antihypertensive dosing options.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhijun (Kevin) Wang PhD, Shein Chung Chow PhD, Moses S. S. Chow PharmD, FCP
Bioequivalence assessment is critical for generic drug approval, but conventional bioequivalent criteria for identical formulations and routes may not be suitable for cross-route comparisons, especially for rapid-acting drugs. Intranasal formulations provide faster absorption and onset of action compared with oral products. At present, establishing bioequivalence across these routes remains challenging. This study proposes a novel method using partial AUCs. Partial AUC (AUC0-ta) was defined as the AUC targeting the rapid absorption phase that better reflects therapeutic onset with ta numerically equal to Tmax + 1 SD for the reference product. To address high variability in cross-route studies, the BE acceptance range for geometric mean ratios of AUC0-ta was widened to 70%–143%. To evaluate this approach, plasma concentration data from two crossover studies comparing intranasal vardenafil (5 and 3.8 mg) with an approved oral tablet (10 mg) were analyzed. Intra- and inter-subject variabilities were estimated. Initial results indicated that bioequivalence could not be demonstrated due to high variability. However, the simulation with optimized dose selection and increased sample size (e.g., 48 subjects) shows that the 90% confidence intervals for both Cmax and AUC0-ta fell within the expanded bioequivalent limits. In contrast, total AUC failed to meet BE requirements, underscoring its limited relevance for rapid-onset, cross-route comparisons. This adapted BE approach may offer a more suitable method for intranasal and other rapid-acting products. With further validation, it could support regulatory approval and improve patient access to fast-acting therapies.
{"title":"Bioequivalence Assessment for Intranasal Rapid-Acting Drug Products","authors":"Zhijun (Kevin) Wang PhD, Shein Chung Chow PhD, Moses S. S. Chow PharmD, FCP","doi":"10.1002/jcph.70138","DOIUrl":"10.1002/jcph.70138","url":null,"abstract":"<p>Bioequivalence assessment is critical for generic drug approval, but conventional bioequivalent criteria for identical formulations and routes may not be suitable for cross-route comparisons, especially for rapid-acting drugs. Intranasal formulations provide faster absorption and onset of action compared with oral products. At present, establishing bioequivalence across these routes remains challenging. This study proposes a novel method using partial AUCs. Partial AUC (AUC<sub>0-ta</sub>) was defined as the AUC targeting the rapid absorption phase that better reflects therapeutic onset with ta numerically equal to T<sub>max</sub> + 1 SD for the reference product. To address high variability in cross-route studies, the BE acceptance range for geometric mean ratios of AUC<sub>0-ta</sub> was widened to 70%–143%. To evaluate this approach, plasma concentration data from two crossover studies comparing intranasal vardenafil (5 and 3.8 mg) with an approved oral tablet (10 mg) were analyzed. Intra- and inter-subject variabilities were estimated. Initial results indicated that bioequivalence could not be demonstrated due to high variability. However, the simulation with optimized dose selection and increased sample size (e.g., 48 subjects) shows that the 90% confidence intervals for both C<sub>max</sub> and AUC<sub>0-ta</sub> fell within the expanded bioequivalent limits. In contrast, total AUC failed to meet BE requirements, underscoring its limited relevance for rapid-onset, cross-route comparisons. This adapted BE approach may offer a more suitable method for intranasal and other rapid-acting products. With further validation, it could support regulatory approval and improve patient access to fast-acting therapies.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145918974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}