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QSP modeling of a transiently inactivating antibody-drug conjugate highlights benefit of short antibody half life. 短暂失活的抗体-药物偶联物的QSP模型突出了抗体半衰期短的好处。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-17 DOI: 10.1007/s10928-024-09956-1
Eshita Khera, Lekshmi Dharmarajan, Dominik Hainzl, Volker Engelhardt, Helena Vostiarova, John Davis, Nicolas Ebel, Kuno Wuersch, Vincent Romanet, Sherif Sharaby, Jeffrey D Kearns

Antibody drug conjugates (ADC) are a promising class of oncology therapeutics consisting of an antibody conjugated to a payload via a linker. DYP688 is a novel ADC comprising of a signaling protein inhibitor payload (FR900359) that undergoes unique on-antibody inactivation in plasma, resulting in complex pharmacology. To assess the impact of FR inactivation on DYP688 pharmacology and clinical developability, we performed translational modeling of preclinical PK and tumor growth inhibition (TGI) data, accompanied by mechanistic Krogh cylinder tumor modeling. Using a PK-TGI model, we identified a composite exposure-above-tumorostatic concentration (AUCTSC) metric as the PK-driver of efficacy. To underpin the mechanisms behind AUCTSC as the driver of efficacy, we performed quantitative systems pharmacology (QSP) modeling of DYP688 intratumoral pharmacokinetics and pharmacodynamics. Through exploratory simulations, we show that by deviating from canonical ADC design dogma, DYP688 has optimal FR900359 activity despite its transient inactivation. Finally, we performed the successful preclinical to clinical translation of DYP688 PK, including the payload inactivation kinetics, evidenced by good agreement of the predicted PK to the observed interim clinical PK. Overall, this work highlights early quantitative pharmacokinetics as a missing link in the ADC design-developability chasm.

抗体药物偶联物(ADC)是一类很有前途的肿瘤治疗药物,由抗体通过连接物偶联到有效载荷上组成。DYP688是一种新型ADC,由信号蛋白抑制剂有效载荷(FR900359)组成,在血浆中经历独特的抗体失活,导致复杂的药理学。为了评估FR失活对DYP688药理学和临床可开发性的影响,我们对临床前PK和肿瘤生长抑制(TGI)数据进行了翻译建模,同时进行了机械Krogh圆柱肿瘤建模。使用PK-TGI模型,我们确定了一个复合暴露于肿瘤以上浓度(AUCTSC)指标作为pk -疗效的驱动因素。为了支持AUCTSC作为疗效驱动因素背后的机制,我们对DYP688的肿瘤内药代动力学和药效学进行了定量系统药理学(QSP)建模。通过探索性仿真,我们表明,尽管DYP688具有瞬时失活,但它偏离了典型的ADC设计教条,具有最佳的FR900359活性。最后,我们成功地完成了DYP688 PK的临床前到临床转化,包括有效载荷失活动力学,证明了预测的PK与观察到的中期临床PK非常一致。总的来说,这项工作强调了早期定量药代动力学是ADC设计-可开发性差距中缺失的一环。
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引用次数: 0
A PopPBPK-RL approach for precision dosing of benazepril in renal impaired patients. 肾损害患者贝那普利精确给药的PopPBPK-RL方法。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-11 DOI: 10.1007/s10928-024-09953-4
Guillermo Vigueras, Lucía Muñoz-Gil, Valerie Reinisch, Joana T Pinto

Current treatment recommendations mainly rely on rule-based protocols defined from evidence-based clinical guidelines, which are difficult to adapt for high-risk patients such as those with renal impairment. Consequently, unsuccessful therapies and the occurrence of adverse drug reactions are common. Within the context of personalized medicine, that tries to deliver the right treatment dose to maximize efficacy and minimize toxicity, the concept of model-informed precision dosing proposes the use of mechanistic models, like physiologically based pharmacokinetic (PBPK) modeling, to predict drug regimes outcomes. Nonetheless, PBPK models have limited capability when computing patients' centric optimized drug doses. Consequently, reinforcement learning (RL) has been previously used to personalize drug dosage. In this work we propose the first PBPK and RL-based precision dosing system for an orally taken drug (benazepril) considering a virtual population of patients with renal disease. Population based PBPK modeling is used in combination with RL for obtaining patient tailored dose regimes. We also perform patient stratification and feature selection to better handle dose tailoring problems. Based on patients' characteristics with best predictive capabilities, benazepril dose regimes are obtained for a population with features' diversity. Obtained regimes are evaluated based on PK parameters considered. Results show that the proof-of-concept approach herein is capable of learning good dosing regimes for most patients. The use of a PopPBPK model allowed to account for intervariability of patient characteristics and be more inclusive considering also non-frequent patients. Impact analysis of patients' features reveals that renal impairment is the main driver affecting RL capabilities.

目前的治疗建议主要依赖于基于证据的临床指南定义的基于规则的方案,这些方案很难适用于肾损害等高危患者。因此,治疗失败和药物不良反应的发生是常见的。在个性化医疗的背景下,试图提供正确的治疗剂量以最大化疗效和最小化毒性,模型知情精确给药的概念建议使用机制模型,如基于生理的药代动力学(PBPK)模型,来预测药物方案的结果。然而,PBPK模型在计算以患者为中心的最佳药物剂量时能力有限。因此,强化学习(RL)先前已被用于个性化药物剂量。在这项工作中,我们提出了第一个基于PBPK和rl的口服药物(贝那普利)精确给药系统,考虑到肾脏疾病患者的虚拟人群。基于人群的PBPK模型与RL结合使用,以获得患者定制的剂量方案。我们还进行患者分层和特征选择,以更好地处理剂量裁剪问题。根据具有最佳预测能力的患者特征,获得具有多样性特征的人群的贝那普利剂量方案。根据所考虑的PK参数对得到的状态进行评估。结果表明,本文的概念验证方法能够为大多数患者学习良好的给药方案。使用PopPBPK模型可以解释患者特征的互变性,并且考虑到非常见患者也更具包容性。对患者特征的影响分析表明,肾脏损害是影响RL能力的主要驱动因素。
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引用次数: 0
Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment. 使用基于人工智能的倾向加权方法估计安慰剂对照临床试验中治疗效果的模型知情方法,以解释对治疗的非特异性反应。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-10 DOI: 10.1007/s10928-024-09950-7
Roberto Gomeni, F Bressolle-Gomeni

In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Treatment effect (TE) is estimated by the baseline-adjusted difference at EOS of TR between active treatments and placebo.The TE is function of treatment-specific and, non-specific (NSRT) effect (referred as placebo effect), and placebo response. The conventional statistical approaches used to estimate TE does not account for the potentially confounding effect of NSRT. This pragmatic approach is equivalent to assume that TE is independent of NSRT even if this assumption is not true, leading to potential risks of inflating false negative/positive results in presence of high proportion of subjects with high/low NSRT.The objective of this study was to develop a model informed framework to analyze the outcomes of RCTs using data driven models, non-linear-mixed effect approach, artificial intelligence, and propensity score weighted methodology (PSW) to control the confounding effect of treatment non-specific response on the estimated TE. The secondary objective was to explore the impact of relevant covariates (including the assessment of a dose-response relationship) on the outcomes of pooled data from two RCTs.The proposed PSW approach provides a critical tool for controlling the confounding effect of treatment non-specific response, to increase signal detection and to provide a reliable estimate of the 'true' treatment effect by controlling false negative results associated with excessively high treatment non-specific response.

在重度抑郁症(MDD)的随机安慰剂对照临床试验(RCT)中,治疗反应(TR)是通过研究结束时用于评估疾病严重程度的临床量表得分的基线变化来估计的。治疗效果(TE)是通过积极治疗和安慰剂之间的基线调整后的TR EOS差异来估计的。TE是治疗特异性和非特异性(NSRT)效应(称为安慰剂效应)和安慰剂反应的函数。用于估计TE的传统统计方法不能解释NSRT的潜在混杂效应。这种实用主义的方法相当于假设TE独立于NSRT,即使这种假设是不正确的,这会导致在高/低NSRT受试者比例存在时夸大假阴性/假阳性结果的潜在风险。本研究的目的是建立一个模型知情框架,使用数据驱动模型、非线性混合效应方法、人工智能和倾向评分加权方法(PSW)分析随机对照试验的结果,以控制治疗非特异性反应对估计TE的混杂效应。次要目的是探讨相关协变量(包括评估剂量-反应关系)对两项随机对照试验汇总数据结果的影响。提出的PSW方法为控制治疗非特异性反应的混淆效应、增加信号检测以及通过控制与过高治疗非特异性反应相关的假阴性结果提供可靠的“真实”治疗效果估计提供了关键工具。
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引用次数: 0
Comparison of the power and type 1 error of total score models for drug effect detection in clinical trials. 临床试验药物疗效检测总分模型的功效及1型误差比较。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-10 DOI: 10.1007/s10928-024-09949-0
Elham Haem, Mats O Karlsson, Sebastian Ueckert

Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available. Therefore, total score models are often utilized. The most popular total score models are continuous variable (CV) models, but this strategy establishes assumptions that go against the integer nature, and typically also the bounded nature, of data. Bounded integer (BI) and Coarsened grid (CG) models respect the nature of the data. However, their power to detect drug effects has not been as thoroughly studied in clinical trials. When an IRT model is accessible, IRT-informed models (I-BI and I-CV) are promising methods in which the mean and variability of the total score at any position are extracted from the existing IRT model. In this study, total score data were simulated from the MDS-UPDRS motor subscale. Then, the power, type 1 error, and treatment effect bias of six total score models for detecting drug effects in clinical trials were explored. Further, it was investigated how the power, type 1 of error, and treatment effect bias for the I-BI and I-CV models were affected by mis-specified item information from the IRT model. The I-BI model demonstrated the highest statistical power, maintained an acceptable Type I error rate, and exhibited minimal bias, approaching zero. Following that, the I-CV, BI, and CG with Czado transformation (CG_Czado) models provided the maximum power. However, the CG_Czado model had inflated type 1 error under low sample size scenarios in each arm of clinical trials. The CG model among total score models displayed the lowest power and the most inflated type 1 error. Therefore, the results favor the I-BI model when an IRT model is available; otherwise, the BI model.

综合量表数据由许多分类问题/项目组成,这些问题/项目通常加总为一个总分,在临床试验中通常被用作主要终点。这些终点在概念上是离散的,在性质上是受限的。项目反应理论(IRT)是在临床试验的综合量表数据中检测药物效应的有效方法,但估计所有参数需要大量样本和所有项目信息,而这些信息可能无法获得。因此,通常采用总分模型。最流行的总分模型是连续变量(CV)模型,但这种策略所建立的假设违背了数据的整数性质,通常也违背了数据的有界性质。有界整数(BI)和粗网格(CG)模型尊重数据的性质。但是,它们在临床试验中检测药物效应的能力还没有得到深入研究。当可以使用 IRT 模型时,IRT-informed 模型(I-BI 和 I-CV)是一种很有前途的方法,它可以从现有的 IRT 模型中提取任意位置总分的平均值和变异性。本研究从 MDS-UPDRS 运动分量表中模拟了总分数据。然后,探讨了六种总分模型在临床试验中检测药物效应的功率、1 型误差和治疗效果偏差。此外,还研究了 I-BI 模型和 I-CV 模型的功率、1 类误差和治疗效果偏差如何受到 IRT 模型中误设项目信息的影响。I-BI 模型显示了最高的统计功率,保持了可接受的 I 类错误率,并显示了最小的偏差,接近零。随后,I-CV、BI 和带有 Czado 变换(CG_Czado)的 CG 模型提供了最大的统计效度。然而,CG_Czado 模型在临床试验各臂样本量较少的情况下,1 类误差会增大。在总分模型中,CG 模型的功率最低,类型 1 误差也最大。因此,在有 IRT 模型的情况下,结果倾向于 I-BI 模型;否则,倾向于 BI 模型。
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引用次数: 0
Translational population target binding model for the anti-FcRn fragment antibody efgartigimod. 抗fcrn片段抗体efgartigimod的翻译群体靶点结合模型。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-05 DOI: 10.1007/s10928-024-09952-5
Sven Hoefman, Tamara van Steeg, Ingrid Ottevaere, Judith Baumeister, Stefaan Rossenu

Efgartigimod is a human IgG1 antibody Fc-fragment that lowers IgG levels through blockade of the neonatal Fc receptor (FcRn) and is being evaluated for the treatment of patients with severe autoimmune diseases mediated by pathogenic IgG autoantibodies. Engineered for increased FcRn affinity at both acidic and physiological pH, efgartigimod can outcompete endogenous IgG binding, preventing FcRn-mediated recycling of IgGs and resulting in increased lysosomal degradation. A population pharmacokinetic-pharmacodynamic (PKPD) model including FcRn binding was developed based on data from two healthy volunteer studies after single and repeated administration of efgartigimod. This model was able to simultaneously describe the serum efgartigimod and total IgG profiles across dose groups, using drug-induced FcRn receptor occupancy as driver of total IgG suppression. The model was expanded to describe the PKPD of efgartigimod in cynomolgus monkeys, rabbits, rats and mice. Most species differences were explainable by including the species-specific in vitro affinity for FcRn binding at pH 7.4 and by allometric scaling of the physiological parameters. In vitro-in vivo scaling proved crucial for translation success: the drug effect was over/underpredicted in rabbits/mice when ignoring the lower/higher binding affinity of efgartigimod for these species versus human, respectively. Given the successful model prediction of the PK and total IgG dynamics across species, it was concluded that the PKPD of efgartigimod can be characterized by target binding. From the model, it is suggested that the initial fast decrease of measurable unbound efgartigimod following dosing is the result of combined clearance of free drug and high affinity target binding, while the relatively slow terminal PK phase reflects release of bound drug from the receptor. High affinity target binding protects the drug from elimination and results in a sustained PD effect characterized by an increase in the IgG degradation rate constant with increasing target receptor occupancy.

Efgartigimod是一种人IgG1抗体Fc片段,通过阻断新生儿Fc受体(FcRn)降低IgG水平,目前正在评估用于治疗由致病性IgG自身抗体介导的严重自身免疫性疾病患者。efgartigimod在酸性和生理pH下都能增强FcRn的亲和力,它可以战胜内源性IgG结合,阻止FcRn介导的IgG再循环,导致溶酶体降解增加。基于两名健康志愿者在单次和重复给药艾夫加替莫德后的数据,建立了包括FcRn结合的群体药代动力学-药效学(PKPD)模型。该模型能够同时描述不同剂量组的血清efgartigimod和总IgG谱,利用药物诱导的FcRn受体占用作为总IgG抑制的驱动因素。将该模型扩展到描述食蟹猴、家兔、大鼠和小鼠中赤霉病的PKPD。大多数物种差异可以通过包括物种特异性的pH 7.4下FcRn结合的体外亲和力和生理参数的异速缩放来解释。体外-体内实验证明了翻译成功的关键:当忽略efgartigimod对这些物种与人类的低/高结合亲和力时,兔/小鼠的药物效应被高估/低估。通过对不同物种间PK和总IgG动态的成功模型预测,我们认为efgartigimod的PKPD可以通过靶标结合来表征。从模型可以看出,给药后可测量的未结合efgartigimod的初始快速下降是游离药物清除和高亲和力靶标结合的结果,而相对缓慢的末端PK期反映了结合药物从受体释放。高亲和力的靶标结合保护药物不被消除,并导致持续的PD效应,其特征是IgG降解率随靶标受体占用率的增加而增加。
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引用次数: 0
Semi-mechanistic population pharmacokinetic modeling of DZIF-10c, a neutralizing antibody against SARS-Cov-2: predicting systemic and lung exposure following inhaled and intravenous administration. 抗SARS-Cov-2中和抗体DZIF-10c的半机械群体药代动力学建模:预测吸入和静脉给药后的全身和肺部暴露
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-05 DOI: 10.1007/s10928-024-09947-2
Sree Kurup, Nieves Velez de Mendizabal, Stephan Becker, Erica Bolella, Dorothy De Sousa, Gerd Fätkenheuer, Henning Gruell, Florian Klein, Jakob J Malin, Ulrike Schmid, Julia Korell

DZIF-10c (BI 767551) is a recombinant human monoclonal antibody of the IgG1 kappa isotype. It acts as a SARS-CoV-2 neutralizing antibody. DZIF-10c has been developed for both systemic exposure by intravenous infusion as well as for specific exposure to the respiratory tract by application as an inhaled aerosol generated by a nebulizer. An integrated preclinical/clinical semi-mechanistic population pharmacokinetic model was developed to characterize the exposure profile of DZIF-10c in the systemic circulation and lungs. To inform and reduce uncertainty around exposure in the lungs following different methods of dosing, preclinical cynomolgus monkey data was combined with human data using allometric scaling principles. Human serum concentrations of DZIF-10c from two clinical trials were combined with serum/plasma and lung epithelial lining fluid (ELF) concentrations from three preclinical studies to characterize the relationship between dosing, serum/plasma, and lung exposure. The final model was used to predict exposure in the lungs following different routes of administration. Simulations showed that inhalation provides immediate and relevant exposure in the lung ELF at a much lower dose compared with an infusion. Combining inhalation with intravenous therapy results in high and sustained DZIF-10c exposure in the lungs and systemic circulation, thereby combining the benefits of both routes of administration. By combining preclinical data with clinical data (via allometric scaling principles), the developed population pharmacokinetic model reduced uncertainty around exposure in the lungs allowing evaluation of alternative dosing strategies to achieve the desired concentrations of DZIF-10c in human lungs.

DZIF-10c (BI 767551)是IgG1 kappa同型的重组人单克隆抗体。它作为一种SARS-CoV-2中和抗体。DZIF-10c既可用于静脉输注的全身暴露,也可用于通过喷雾器产生的吸入气溶胶应用于呼吸道的特定暴露。建立了一个综合临床前/临床半机械人群药代动力学模型,以表征DZIF-10c在体循环和肺部的暴露谱。为了了解和减少不同给药方法对肺部暴露的不确定性,使用异速缩放原理将临床前食蟹猴数据与人类数据相结合。将两项临床试验的人血清DZIF-10c浓度与三项临床前研究的血清/血浆和肺上皮衬里液(ELF)浓度相结合,以表征剂量、血清/血浆和肺暴露之间的关系。最后的模型用于预测不同给药途径对肺部的暴露。模拟结果表明,与输注相比,吸入能以低得多的剂量立即暴露在肺ELF中。吸入与静脉治疗相结合可导致DZIF-10c在肺部和体循环中的高且持续暴露,从而结合两种给药途径的益处。通过结合临床前数据和临床数据(通过异速缩放原则),开发的人群药代动力学模型减少了肺部暴露的不确定性,从而可以评估替代给药策略,以达到人体肺部所需的DZIF-10c浓度。
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引用次数: 0
Multiorgan-on-a-chip for cancer drug pharmacokinetics-pharmacodynamics (PK-PD) modeling and simulations. 用于癌症药物药代动力学-药效学(PK-PD)建模和模拟的多器官芯片。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-04 DOI: 10.1007/s10928-024-09955-2
Abdurehman Eshete Mohammed, Filiz Kurucaovalı, Devrim Pesen Okvur

Cancer is one of the most common and fatal diseases worldwide and kills millions of people every year. Cancer drug resistance, lack of efficacy, and safety are significant problems in cancer patients. A multiorgan-on-a-chip (MOC) device consisting of breast and liver compartments was designed with AutoCAD software. The MOC molds were printed by a Formlabs Form 2 3D printer. MDA-MB-231, HepG2, and MCF-10 A cells were used for the MOC experiments. The cell lines were cultured at 37 °C with 5% CO2, and cell viability was assessed via Alamar blue dye to generate pharmacodynamics (PD) data. Drug concentrations from the cell culture media were analyzed via Agilent 1260 Infinity II HPLC with a Waters Symmetry C18 column and used to generate pharmacokinetics (PK) data. The PK and PD data were modeled and simulated by Monolix and Simulix software, respectively. The safety and efficacy of drug dosing regimens were compared, and the best dosing regimens were selected. This research designed and fabricated a unique MOC consisting of liver and breast compartments that overcomes the need for sealing or assembling. It was used for PK-PD modeling and simulations, and its functionality was proven experimentally. The new MOC will be helpful in preclinical trials to evaluate the efficacy and safety of drugs.

癌症是世界上最常见、最致命的疾病之一,每年夺去数百万人的生命。癌症耐药、缺乏疗效和安全性是困扰癌症患者的重要问题。利用AutoCAD软件设计了一种由乳腺和肝室组成的多器官芯片(MOC)装置。MOC模具由Formlabs Form 2 3D打印机打印。使用MDA-MB-231、HepG2和mcf - 10a细胞进行MOC实验。在37℃、5% CO2条件下培养细胞系,用Alamar蓝染料测定细胞活力,生成药效学(PD)数据。通过Agilent 1260 Infinity II高效液相色谱柱(Waters Symmetry C18柱)分析细胞培养基中的药物浓度,并生成药代动力学(PK)数据。分别用Monolix和Simulix软件对PK和PD数据进行建模和仿真。比较不同给药方案的安全性和有效性,选择最佳给药方案。本研究设计并制造了一种独特的MOC,由肝脏和乳房隔室组成,克服了密封或组装的需要。将其用于PK-PD建模和仿真,并通过实验验证了其功能。新的MOC将有助于临床前试验评估药物的有效性和安全性。
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引用次数: 0
Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations. 深隔室模型中的混合效应估计:变量方法优于一阶近似方法
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-01 Epub Date: 2024-07-04 DOI: 10.1007/s10928-024-09931-w
Alexander Janssen, Frank C Bennis, Marjon H Cnossen, Ron A A Mathôt

This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated using variational distributions whose parameters can be directly optimized. We found that variational approximations estimated using the path derivative gradient estimator version of VI were highly accurate. Models fit on the simulated data set using the FO and VI objective functions gave similar results, with accurate predictions of both the population parameters and covariate effects. Contrastingly, models fit using FOCE depicted erratic behaviour during optimization, and resulting parameter estimates were inaccurate. Finally, we compared the performance of the methods on two real-world data sets of haemophilia A patients who received standard half-life factor VIII concentrates during prophylactic and perioperative settings. Again, models fit using FO and VI depicted similar results, although some models fit using FO presented divergent results. Again, models fit using FOCE were unstable. In conclusion, we show that mixed-effects estimation using the DCM is feasible. VI performs conditional estimation, which might lead to more accurate results in more complex models compared to the FO method.

这项工作的重点是将深隔室模型(DCM)框架扩展到混合效应的估算。通过引入随机效应,可以根据药物测量结果对模型预测进行个性化处理,从而对不同的治疗方案进行个体化测试。在一项模拟研究中,比较了经典的一阶算法(FO 和 FOCE)和基于机器学习的变异推理算法(VI)的性能。在变异推理中,随机变量的后验分布通过变异分布来近似,其参数可以直接优化。我们发现,使用路径导数梯度估计器版本的 VI 估算的变分近似值非常准确。在模拟数据集上使用 FO 和 VI 目标函数拟合的模型结果相似,都能准确预测群体参数和协变效应。相反,使用 FOCE 拟合的模型在优化过程中表现不稳定,得出的参数估计也不准确。最后,我们比较了这两种方法在两个真实世界数据集上的表现,这两个数据集是在预防和围手术期接受标准半衰期第八因子浓缩液治疗的 A 型血友病患者。同样,使用 FO 和 VI 拟合的模型显示了相似的结果,但使用 FO 拟合的一些模型显示了不同的结果。同样,使用 FOCE 拟合的模型也不稳定。总之,我们表明使用 DCM 进行混合效应估计是可行的。与 FO 方法相比,VI 可以进行条件估计,这可能会在更复杂的模型中得到更准确的结果。
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引用次数: 0
A review of the physiological effects of microgravity and innovative formulation for space travelers. 回顾微重力的生理影响和针对太空旅行者的创新配方。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-01 Epub Date: 2024-08-20 DOI: 10.1007/s10928-024-09938-3
Jey Kumar Pachiyappan, Manali Patel, Parikshit Roychowdhury, Imrankhan Nizam, Raagul Seenivasan, Swathi Sudhakar, M R Jeyaprakash, Veera Venkata Satyanarayana Reddy Karri, Jayakumar Venkatesan, Priti Mehta, Sudhakar Kothandan, Indhumathi Thirugnanasambandham, Gowthamarajan Kuppusamy

During the space travel mission, astronauts' physiological and psychological behavior will alter, and they will start consuming terrestrial drug products. However, factors such as microgravity, radiation exposure, temperature, humidity, strong vibrations, space debris, and other issues encountered, the drug product undergo instability This instability combined with physiological changes will affect the shelf life and diminish the pharmacokinetic and pharmacodynamic profile of the drug product. Consequently, the physicochemical changes will produce a toxic degradation product and a lesser potency dosage form which may result in reduced or no therapeutic action, so the astronaut consumes an additional dose to remain healthy. On long-duration missions like Mars, the drug product cannot be replaced, and the astronaut may relay on the available medications. Sometimes, radiation-induced impurities in the drug product will cause severe problems for the astronaut. So, this review article highlights the current state of various space-related factors affecting the drug product and provides a comprehensive summary of the physiological changes which primarly focus on absorption, distribution, metabolism, and excretion (ADME). Along with that, we insist some of the strategies like novel formulations, space medicine manufacturing from plants, and 3D printed medicine for astronauts in longer-duration missions. Such developments are anticipated to significantly contribute to new developments with applications in both human space exploration and on terrestrial healthcare.

在执行太空旅行任务期间,宇航员的生理和心理行为会发生变化,并开始服用地球上的药物产品。然而,由于受到微重力、辐射、温度、湿度、强烈振动、太空碎片等因素的影响,药物产品会出现不稳定的情况,这种不稳定性加上生理变化会影响药物产品的保质期,并降低药物产品的药代动力学和药效学特征。因此,理化变化将产生有毒的降解产物和药效较低的剂型,从而可能导致治疗作用降低或无效,因此宇航员需要额外服用剂量以保持健康。在火星等长时间飞行任务中,药物产品无法更换,宇航员只能依靠现有药物。有时,药物中由辐射引起的杂质会给宇航员带来严重问题。因此,这篇综述文章重点介绍了影响药物的各种太空相关因素的现状,并全面总结了主要集中在吸收、分布、代谢和排泄(ADME)方面的生理变化。此外,我们还坚持采用一些策略,如新型配方、利用植物制造太空药物,以及为执行更长时间任务的宇航员提供 3D 打印药物。预计这些发展将极大地促进人类太空探索和地面医疗保健应用的新发展。
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引用次数: 0
Computing optimal drug dosing regarding efficacy and safety: the enhanced OptiDose method in NONMEM. 计算疗效和安全性方面的最佳药物剂量:NONMEM 中增强的 OptiDose 方法。
IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pub Date : 2024-12-01 Epub Date: 2024-10-08 DOI: 10.1007/s10928-024-09940-9
Freya Bachmann, Gilbert Koch, Robert J Bauer, Britta Steffens, Gabor Szinnai, Marc Pfister, Johannes Schropp

Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method.

最近,我们开发了一种最佳用药剂量算法(OptiDose),用于计算任何药物计量学模型在给定剂量情况下的最佳用药剂量。在本研究中,我们对 OptiDose 概念进行了改进,以计算疗效和安全性目标方面的最佳药物剂量。通常,这两个目标的重要性并不相同,但其中一个是首要目标,必须满足,而另一个是次要目标,应在不影响首要目标的前提下尽可能实现。从数学上讲,这导致了状态受限的最优控制问题。本文阐述了如何设置此类问题,并将其转化为可在 NONMEM 中求解的经典无约束最优控制问题。三个不同的优化配料任务说明了所提出的增强型 OptiDose 方法的影响。
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引用次数: 0
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Journal of Pharmacokinetics and Pharmacodynamics
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