J Pharmacokinet Pharmacodyn [JOURNAL]
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200 papers
Population pharmacokinetics of the dual endothelin receptor antagonist aprocitentan in subjects with or without essential or resistant hypertension.
Brussee JM, Sidharta PN, Dingemanse J … +1 more , Krause A
J Pharmacokinet Pharmacodyn · 2024 Jun · PMID 38332190 · Publisher ↗
Aprocitentan is a novel, potent, dual endothelin receptor antagonist that recently demonstrated efficacy in the treatment of difficult-to-treat (resistant) hypertension. The aim of this study was to develop a population...
Aprocitentan is a novel, potent, dual endothelin receptor antagonist that recently demonstrated efficacy in the treatment of difficult-to-treat (resistant) hypertension. The aim of this study was to develop a population pharmacokinetic (PK) model describing aprocitentan plasma concentration over time, to investigate relationships between subject-specific factors (covariates) and model parameters, and to quantify the influence of the identified covariates on the exposure to aprocitentan via model-based simulations, enabling judgment about the clinical relevance of the covariates.PK data from 902 subjects in ten Phase 1, one Phase 2, and one Phase 3 study were pooled to develop a joint population PK model. The concentration-time course of aprocitentan was described by a two-compartment model with absorption lag time, first-order absorption and elimination, and reduced relative bioavailability following very high doses of 300 and 600 mg.The population PK model described the observed data well. Volume and clearance parameters were associated with body weight. Renal function as reflected by estimated glomerular filtration rate (eGFR), hepatic impairment, and sex were identified as relevant covariates on clearance.The subject-specific characteristics of body weight, eGFR, hepatic impairment, and sex were shown to influence exposure parameters area under the concentration-time curve and maximum concentration in steady state to a limited extent, i.e., not more than 25% different from a reference subject, and therefore do not warrant dose adjustments.
A population pharmacokinetics model of balovaptan to support dose selection in adult and pediatric populations.
Schaedeli Stark F, Chavanne C, Derks M … +5 more , Jolling K, Lagraauw HM, Lindbom L, Prins K, Silber Baumann HE
J Pharmacokinet Pharmacodyn · 2024 Jun · PMID 38308741 · Full text
Balovaptan is a brain-penetrating vasopressin receptor 1a antagonist previously investigated for the core symptoms of autism spectrum disorder (ASD). A population pharmacokinetic (PK) model of balovaptan was developed, i...
Balovaptan is a brain-penetrating vasopressin receptor 1a antagonist previously investigated for the core symptoms of autism spectrum disorder (ASD). A population pharmacokinetic (PK) model of balovaptan was developed, initially to assist clinical dosing for adult and pediatric ASD studies and subsequently for new clinical indications including malignant cerebral edema (MCE) and post-traumatic stress disorder. The final model incorporates one-compartment disposition and describes time- and dose-dependent non-linear PK through empirical drug binding and a gut extraction component with turnover. An age effect on clearance observed in children was modeled by an asymptotic function that predicts adult-equivalent exposures at 40% of the adult dose for children aged 2-4 years, 70% for 5-9 years, and at the full adult dose for ≥ 10 years. The model was adapted for intravenous (IV) balovaptan dosing and combined with in vitro and ex vivo pharmacodynamic data to simulate brain receptor occupancy as a guide for dosing in a phase II trial of MCE prophylaxis after acute ischemic stroke. A sequence of three stepped-dose daily infusions of 50, 25 and 15 mg over 30 or 60 min was predicted to achieve a target occupancy of ≥ 80% in ≥ 95% of patients over a 3-day period. This model predicts both oral and IV balovaptan exposure across a wide age range and will be a valuable tool to analyze and predict its PK in new indications and target populations, including pediatric patients.
Correction: External control arms for rare diseases: building a body of supporting evidence.
Khachatryan A, Read SH, Madison T
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 38243007 · Full text
Abstract loading — click title to view on PubMed.
How drug onset rate and duration of action affect drug forgiveness.
Clark ED, Lawley SD
J Pharmacokinet Pharmacodyn · 2024 Jun · PMID 38198076 · Publisher ↗
Medication nonadherence is one of the largest problems in healthcare today, particularly for patients undergoing long-term pharmacotherapy. To combat nonadherence, it is often recommended to prescribe so-called "forgivin...
Medication nonadherence is one of the largest problems in healthcare today, particularly for patients undergoing long-term pharmacotherapy. To combat nonadherence, it is often recommended to prescribe so-called "forgiving" drugs, which maintain their effect despite lapses in patient adherence. Nevertheless, drug forgiveness is difficult to quantify and compare between different drugs. In this paper, we construct and analyze a stochastic pharmacokinetic/pharmacodynamic (PK/PD) model to quantify and understand drug forgiveness. The model parameterizes a medication merely by an effective rate of onset of effect when the medication is taken (on-rate) and an effective rate of loss of effect when a dose is missed (off-rate). Patient dosing is modeled by a stochastic process that allows for correlations in missed doses. We analyze this "on/off" model and derive explicit formulas that show how treatment efficacy depends on drug parameters and patient adherence. As a case study, we compare the effects of nonadherence on the efficacy of various antihypertensive medications. Our analysis shows how different drugs can have identical efficacies under perfect adherence, but vastly different efficacies for adherence patterns typical of actual patients. We further demonstrate that complex PK/PD models can indeed be parameterized in terms of effective on-rates and off-rates. Finally, we have created an online app to allow pharmacometricians to explore the implications of our model and analysis.
Semi-mechanistic modeling of resistance development to β-lactam and β-lactamase-inhibitor combinations.
Tandar ST, Aulin LBS, Leemkuil EMJ … +2 more , Liakopoulos A, van Hasselt JGC
J Pharmacokinet Pharmacodyn · 2024 Jun · PMID 38008877 · Publisher ↗
The use of β-lactam (BL) and β-lactamase inhibitor (BLI) combinations, such as piperacillin-tazobactam (PIP-TAZ) is an effective strategy to combat infections by extended-spectrum β-lactamase-producing bacteria. However,...
The use of β-lactam (BL) and β-lactamase inhibitor (BLI) combinations, such as piperacillin-tazobactam (PIP-TAZ) is an effective strategy to combat infections by extended-spectrum β-lactamase-producing bacteria. However, in Gram-negative bacteria, resistance (both mutational and adaptive) to BL-BLI combination can still develop through multiple mechanisms. These mechanisms may include increased β-lactamase activity, reduced drug influx, and increased drug efflux. Understanding the relative contribution of these mechanisms during resistance development helps identify the most impactful mechanism to target in designing a treatment to counter BL-BLI resistance. This study used semi-mechanistic mathematical modeling in combination with antibiotic sensitivity assays to assess the potential impact of different resistance mechanisms during the development of PIP-TAZ resistance in a Klebsiella pneumoniae isolate expressing CTX-M-15 and SHV-1 β-lactamases. The mathematical models were used to evaluate the potential impact of several cellular changes as a sole mediator of PIP-TAZ resistance. Our semi-mechanistic model identified 2 out of the 13 inspected mechanisms as key resistance mechanisms that may independently support the observed magnitude of PIP-TAZ resistance, namely porin loss and efflux pump up-regulation. Simulation using the resulting models also suggested the possible adjustment of PIP-TAZ dose outside its commonly used 8:1 dosing ratio. The current study demonstrated how theory-based mechanistic models informed by experimental data can be used to support hypothesis generation regarding potential resistance mechanisms, which may guide subsequent experimental studies.
Comparison of monoclonal antibody disposition predictions using different physiologically based pharmacokinetic modelling platforms.
De Sutter PJ, Gasthuys E, Vermeulen A
J Pharmacokinet Pharmacodyn · 2024 Dec · PMID 37952005 · Publisher ↗
Physiologically based pharmacokinetic (PBPK) models can be used to leverage physiological and in vitro data to predict monoclonal antibody (mAb) concentrations in serum and tissues. However, it is currently not known how...
Physiologically based pharmacokinetic (PBPK) models can be used to leverage physiological and in vitro data to predict monoclonal antibody (mAb) concentrations in serum and tissues. However, it is currently not known how consistent predictions of mAb disposition are across PBPK modelling platforms. In this work PBPK simulations of IgG, adalimumab and infliximab were compared between three platforms (Simcyp, PK-Sim, and GastroPlus). Accuracy of predicted serum and tissue concentrations was assessed using observed data collected from the literature. Physiological and mAb related input parameters were also compared and sensitivity analyses were carried out to evaluate model behavior when input values were altered. Differences in serum kinetics of IgG between platforms were minimal for a dose of 1 mg/kg, but became more noticeable at higher dosages (> 100 mg/kg) and when reference (healthy) physiological input values were altered. Predicted serum concentrations of both adalimumab and infliximab were comparable across platforms, but were noticeably higher than observed values. Tissue concentrations differed remarkably between the platforms, both for total- and interstitial fluid (ISF) concentrations. The accuracy of total tissue concentrations was within a three-fold of observed values for all tissues, except for brain tissue concentrations, which were overpredicted. Predictions of tissue ISF concentrations were less accurate and were best captured by GastroPlus. Overall, these simulations show that the different PBPK platforms generally predict similar mAb serum concentrations, but variable tissue concentrations. Caution is therefore warranted when PBPK models are used to simulate effect site tissue concentrations of mAbs without data to verify the predictions.
Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model.
Shin E, Ramanathan M
J Pharmacokinet Pharmacodyn · 2024 Apr · PMID 37952004 · Publisher ↗
To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartm...
To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semi-logarithmic graphing of concentration-time profiles and calculating area under the curve and area under the moment curve from time zero to infinity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.
Expansion of platform physiologically-based pharmacokinetic model for monoclonal antibodies towards different preclinical species: cats, sheep, and dogs.
Huang HW, Wu S, Chowdhury EA … +1 more , Shah DK
J Pharmacokinet Pharmacodyn · 2024 Dec · PMID 37947924 · Publisher ↗
Monoclonal antibodies (mAbs) are becoming an important therapeutic option in veterinary medicine, and understanding the pharmacokinetic (PK) of mAbs in higher-order animal species is also important for human drug develop...
Monoclonal antibodies (mAbs) are becoming an important therapeutic option in veterinary medicine, and understanding the pharmacokinetic (PK) of mAbs in higher-order animal species is also important for human drug development. To better understand the PK of mAbs in these animals, here we have expanded a platform physiological-based pharmacokinetic (PBPK) model to characterize the disposition of mAbs in three different preclinical species: cats, sheep, and dogs. We obtained PK data for mAbs and physiological parameters for the three different species from the literature. We were able to describe the PK of mAbs following intravenous (IV) or subcutaneous administration in cats, IV administration in sheep, and IV administration dogs reasonably well by fixing the physiological parameters and just estimating the parameters related to the binding of mAbs to the neonatal Fc receptor. The platform PBPK model presented here provides a quantitative tool to predict the plasma PK of mAbs in dogs, cats, and sheep. The model can also predict mAb PK in different tissues where the site of action might be located. As such, the mAb PBPK model presented here can facilitate the discovery, development, and preclinical-to-clinical translation of mAbs for veterinary and human medicine. The model can also be modified in the future to account for more detailed compartments for certain organs, different pathophysiology in the animals, and target-mediated drug disposition.
Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach.
Brekkan A, Lledo-Garcia R, Lacroix B … +3 more , Jönsson S, Karlsson MO, Plan EL
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37943398 · Full text
Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug dispo...
Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.
Explaining in-vitro to in-vivo efficacy correlations in oncology pre-clinical development via a semi-mechanistic mathematical model.
Huber HJ, Mistry HB
J Pharmacokinet Pharmacodyn · 2024 Apr · PMID 37930506 · Full text
In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they...
In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they lack a deeper understanding of the underlying mechanisms. Our paper therefore focuses on linking empirical IVIVC relations for small-molecule kinase inhibitors with a semi-mechanistic tumour-growth model. We develop an approach incorporating parameters like the compound's peak-trough ratio (PTR), Hill coefficient of in-vitro dose-response curves, and xenograft-specific properties. This leads to formulas for determining efficacious doses for tumor stasis under linear pharmacokinetics equivalent to traditional empirical IVIVC relations, but enabling more systematic analysis. Our findings reveal that in-vivo xenograft-specific parameters, specifically the growth rate (g) and decay rate (d), along with the average exposure, are generally more significant determinants of tumor stasis and effective dose than the compound's peak-trough ratio. However, as the Hill coefficient increases, the dependency of tumor stasis on the PTR becomes more pronounced, indicating that the compound is more influenced by its maximum or trough values rather than the average exposure. Furthermore, we discuss the translation of our method to predict population dose ranges in clinical studies and propose a resistance mechanism that solely relies on specific in-vivo xenograft parameters instead of IC50 exposure coverage. In summary, our study aims to provide a more mechanistic understanding of IVIVC relations, emphasizing the importance of xenograft-specific parameters and PTR on tumor stasis.
Rare oncology therapeutics: review of clinical pharmacology package of drug approvals (2019-2023) by US FDA, best practices and recommendations.
Mitra A, Lee JB, Steinbach D … +2 more , Hazra A, Krishna R
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37925369 · Publisher ↗
There are many challenges with rare diseases drug development and rare oncology indications are not different. To understand the regulatory landscape as it relates to application of clinical pharmacology principles in ra...
There are many challenges with rare diseases drug development and rare oncology indications are not different. To understand the regulatory landscape as it relates to application of clinical pharmacology principles in rare oncology product development, we reviewed publicly available information of 39 approvals by US FDA between January 2019 and March 2023. The objective was to understand the expected clinical pharmacology studies and knowledge base in such approvals. Model informed drug development (MIDD) applications were also reviewed, as such approaches are expected to play a critical role in filling clinical pharmacology gaps in rare oncology, where number of clinical trials and size of these trials will perhaps continue to be small. The findings highlighted how clinical pharmacology contributed to the evidence of effectiveness, dose optimization and elucidation of intrinsic and extrinsic factors affecting drug's behavior. Clinical pharmacology studies were often integrated with modeling in many of the NDAs/BLAs. Of the post marketing requirements (PMR) received, 18% were for dose optimization, 49% for DDI, 8% for QTc, 49% for specific population, and 5% for food effect. Two post marketing commitments (PMC) were issued for immunogenicity of the 11 biologics submissions. 15% (6 of 39) of the submissions used maximum tolerated dose (MTD) to advance their molecule into Phase 2 studies. Of them 3 approvals received PMR for dose optimization. 3 + 3 was the most prevalent Phase 1 design with use in 74% of the New Drug Applications (NDA)/Biologic License Applications (BLA) reviewed. Rest used innovative approaches such as BLRM, BOIN or mTPi, with BLRM being the most common. Seamless clinical pharmacology and MIDD approaches are paramount for rare oncology drug development.
Learning pharmacometric covariate model structures with symbolic regression networks.
Wahlquist Y, Sundell J, Soltesz K
J Pharmacokinet Pharmacodyn · 2024 Apr · PMID 37864654 · Full text
Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and presen...
Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relationships, such approximations are not human-readable and come at the risk of poor generalizability due to high model complexity.In the present study, a novel methodology for the simultaneous selection of covariate model structure and optimization of its parameters is proposed. It is based on symbolic regression, posed as an optimization problem with a smooth loss function. This enables training of the model through back-propagation using efficient gradient computations.Feasibility and effectiveness are demonstrated by application to a clinical pharmacokinetic data set for propofol, containing infusion and blood sample time series from 1031 individuals. The resulting model is compared to a published state-of-the-art model for the same data set. Our methodology finds a covariate model structure and corresponding parameter values with a slightly better fit, while relying on notably fewer covariates than the state-of-the-art model. Unlike contemporary practice, finding the covariate model structure is achieved without an iterative procedure involving manual interactions.
Population pharmacokinetics and pharmacodynamics of efmarodocokin alfa (IL-22Fc).
Yu Y, Rothenberg ME, Ding HT … +7 more , Brekkan A, Sperinde G, Harder B, Zhang R, Owen R, Kassir N, Lekkerkerker AN
J Pharmacokinet Pharmacodyn · 2024 Apr · PMID 37864000 · Publisher ↗
Efmarodocokin alfa (IL-22Fc) is a fusion protein of human IL-22 linked to the crystallizable fragment (Fc) of human IgG4. It has been tested in multiple indications including inflammatory bowel disease (IBD). The purpose...
Efmarodocokin alfa (IL-22Fc) is a fusion protein of human IL-22 linked to the crystallizable fragment (Fc) of human IgG4. It has been tested in multiple indications including inflammatory bowel disease (IBD). The purposes of the present analyses were to describe the population pharmacokinetics (PK) of efmarodocokin alfa and perform pharmacodynamic (PD) analysis on the longitudinal changes of the PD biomarker REG3A after efmarodocokin alfa treatment as well as identify covariates that affect efmarodocokin alfa PK and REG3A PD. The data used for this analysis included 182 subjects treated with efmarodocokin alfa in two clinical studies. The population PK and PD analyses were conducted sequentially. Efmarodocokin alfa concentration-time data were analyzed using a nonlinear mixed-effects modeling approach, and an indirect response model was adopted to describe the REG3A PD data with efmarodocokin alfa serum concentration linked to the increase in REG3A. The analysis software used were NONMEM and R. A 3-compartment model with linear elimination best described the PK of efmarodocokin alfa. The estimated population-typical value for clearance (CL) was 1.12 L/day, and volume of central compartment was 6.15 L. Efmarodocokin alfa CL increased with higher baseline body weight, C-reactive protein, and CL was 27.6% higher in IBD patients compared to healthy subjects. The indirect response PD model adequately described the longitudinal changes of REG3A after efmarodocokin alfa treatment. A popPK and PD model for efmarodocokin alfa and REG3A was developed and covariates affecting the PK and PD were identified.
Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy.
Choules MP, Bonate PL, Heo N … +1 more , Weddell J
J Pharmacokinet Pharmacodyn · 2024 Oct · PMID 37848637 · Full text
Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a st...
Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.
Low-dimensional neural ODEs and their application in pharmacokinetics.
Bräm DS, Nahum U, Schropp J … +2 more , Pfister M, Koch G
J Pharmacokinet Pharmacodyn · 2024 Apr · PMID 37837491 · Full text
Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamic...
Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.
Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs).
Scheuher B, Ghusinga KR, McGirr K … +5 more , Nowak M, Panday S, Apgar J, Subramanian K, Betts A
J Pharmacokinet Pharmacodyn · 2024 Oct · PMID 37787918 · Full text
A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and tra...
A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and trastuzumab-DXd) were used for model development, calibration, and validation. The model integrates drug specific experimental data including in vitro cellular disposition data, pharmacokinetic (PK) and tumor growth inhibition (TGI) data for T-DM1 and T-DXd, as well as system specific data such as properties of HER2, tumor growth rates, and volumes. The model incorporates mechanistic detail at the intracellular level, to account for different mechanisms of ADC processing and payload release. It describes the disposition of the ADC, antibody, and payload inside and outside of the tumor, including binding to off-tumor, on-target sinks. The resulting multiscale PK model predicts plasma and tumor concentrations of ADC and payload. Tumor payload concentrations predicted by the model were linked to a TGI model and used to describe responses following ADC administration to xenograft mice. The model was translated to humans and virtual clinical trial simulations were performed that successfully predicted progression free survival response for T-DM1 and T-DXd for the treatment of HER2+ metastatic breast cancer, including differential efficacy based upon HER2 expression status. In conclusion, the presented model is a step toward a platform QSP model and strategy for ADCs, integrating multiple types of data and knowledge to predict ADC efficacy. The model has potential application to facilitate ADC design, lead candidate selection, and clinical dosing schedule optimization.
Correction to: Training the next generation of pharmacometric modelers: a multisector perspective.
Bonate PL, Barrett JS, Ait-Oudhia S … +16 more , Brundage R, Corrigan B, Duffull S, Gastonguay M, Karlsson MO, Kijima S, Krause A, Lovern M, Riggs MM, Neely M, Ouellet D, Plan EL, Rao GG, Standing J, Wilkins J, Zhu H
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37670078 · Publisher ↗
Abstract loading — click title to view on PubMed.
Physiologically based pharmacokinetic model to predict drug-drug interactions with the antibody-drug conjugate enfortumab vedotin.
Choules MP, Zuo P, Otsuka Y … +3 more , Garg A, Tang M, Bonate P
J Pharmacokinet Pharmacodyn · 2024 Oct · PMID 37632598 · Full text
Enfortumab vedotin is an antibody-drug conjugate (ADC) comprised of a Nectin-4-directed antibody and monomethyl auristatin E (MMAE), which is primarily eliminated through P-glycoprotein (P-gp)-mediated excretion and cyto...
Enfortumab vedotin is an antibody-drug conjugate (ADC) comprised of a Nectin-4-directed antibody and monomethyl auristatin E (MMAE), which is primarily eliminated through P-glycoprotein (P-gp)-mediated excretion and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. A physiologically based pharmacokinetic (PBPK) model was developed to predict effects of combined P-gp with CYP3A4 inhibitor/inducer (ketoconazole/rifampin) on MMAE exposure when coadministered with enfortumab vedotin and study enfortumab vedotin with CYP3A4 (midazolam) and P-gp (digoxin) substrate exposure. A PBPK model was built for enfortumab vedotin and unconjugated MMAE using the PBPK simulator ADC module. A similar model was developed with brentuximab vedotin, an ADC with the same valine-citrulline-MMAE linker as enfortumab vedotin, for MMAE drug-drug interaction (DDI) verification using clinical data. The DDI simulation predicted a less-than-2-fold increase in MMAE exposure with enfortumab vedotin plus ketoconazole (MMAE geometric mean ratio [GMR] for maximum concentration [C], 1.15; GMR for area under the time-concentration curve from time 0 to last quantifiable concentration [AUC], 1.38). Decreased MMAE exposure above 50% but below 80% was observed with enfortumab vedotin plus rifampin (MMAE GMR C, 0.72; GMR AUC, 0.47). No effect of enfortumab vedotin on midazolam or digoxin systemic exposure was predicted. Results suggest that combination enfortumab vedotin, P-gp, and a CYP3A4 inhibitor may result in increased MMAE exposure and patients should be monitored for potential adverse effects. Combination P-gp and a CYP3A4 inducer may result in decreased MMAE exposure. No exposure change is expected for CYP3A4 or P-gp substrates when combined with enfortumab vedotin.ClinicalTrials.gov identifier Not applicable.
Training the next generation of pharmacometric modelers: a multisector perspective.
Bonate PL, Barrett JS, Ait-Oudhia S … +16 more , Brundage R, Corrigan B, Duffull S, Gastonguay M, Karlsson MO, Kijima S, Krause A, Lovern M, Riggs MM, Neely M, Ouellet D, Plan EL, Rao GG, Standing J, Wilkins J, Zhu H
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37573528 · Publisher ↗
The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed i...
The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.
Population pharmacokinetic/pharmacodynamic modeling of nifekalant injection with varies dosing plan in Chinese volunteers: a randomized, blind, placebo-controlled study.
Jiang J, Xu L, Chai L … +6 more , Zhang L, Liu H, Yan Y, Guan X, Sun H, Tian L
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37566244 · Publisher ↗
Nifekalant hydrochloride is a class III antiarrhythmic agent which could increase the duration of the action potential and the effective refractory period of ventricular and atrial myocytes by blocking the K current. Nif...
Nifekalant hydrochloride is a class III antiarrhythmic agent which could increase the duration of the action potential and the effective refractory period of ventricular and atrial myocytes by blocking the K current. Nifekalant is used to prevent ventricular tachycardia/ventricular fibrillation. QT interval prolongation is the main measurable drug effect. However, due to the complicated dosing plan in clinic, the relationship among dosage, time, drug concentration and efficacy is not fully understood. In this study, a single-center, randomized, blind, dose-ascending, placebo-controlled study was conducted to explore the intrinsic characteristics of nifekalant injection in healthy Chinese volunteers by a population pharmacokinetic (PK)-pharmacodynamic (PD) model approach. 42 subjects were enrolled in this study and received one of three dose plans (loading dose on Day 1 (0.15, 0.3 or 0.5 mg/kg), loading dose followed by maintenance dose (0.2, 0.4 or 0.8 mg/kg/h) on Day 4) or vehicle. Blood samples were drawn for PK evaluation, and ECGs were recorded for QTc calculation at the designed timepoints. No Torsades de Pointes occurred during the study. The popPK model of nifekalant injection could be described by a two-compartment model with first-order elimination. The population mean clearance (CL) was 53.8 L/h. The population mean distribution volume of the central (V) and peripheral (V) compartments was 8.27 L and 45.6 L, respectively. A nonlinear dose-response (E) model well described the pharmacodynamic effect (QTc interval prolongation) of nifekalant. The E and EC from current study were 101 ms and 342 ng/mL, respectively.
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