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Article ; Online: Contributions of William Jusko to Development of Pharmacokinetic and Pharmacodynamic Models and Methods.

Mager, Donald E / Straubinger, Robert M

2023 Volume 113, Issue 1, Page(s) 2–10

MeSH term(s)	Models, Biological ; Pharmacokinetics
Language	English
Publishing date	2023-09-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	3151-3
ISSN	1520-6017 ; 0022-3549
ISSN (online)	1520-6017
ISSN	0022-3549
DOI	10.1016/j.xphs.2023.09.019
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Article ; Online: Systems model identifies baseline cytokine concentrations as potential predictors of rheumatoid arthritis inflammatory response to biologics.

Nakada, Tomohisa / Mager, Donald E

British journal of pharmacology

2022 Volume 179, Issue 16, Page(s) 4063–4077

Abstract: Background and purpose: Circulating cytokines are central pathological mediators of inflammatory autoimmune diseases like rheumatoid arthritis. Immunological diversity in patients might contribute to inadequate responses to biological drugs. To address ... ...

Abstract	Background and purpose: Circulating cytokines are central pathological mediators of inflammatory autoimmune diseases like rheumatoid arthritis. Immunological diversity in patients might contribute to inadequate responses to biological drugs. To address this therapeutic challenge, we developed a mathematical model that simultaneously describes temporal patterns of drug disposition for several biologics and their corresponding targeted cytokines, which were linked to triggering inflammatory responses. Experimental approach: A modelling framework was applied to rheumatoid arthritis-relevant cytokines regulating C-reactive protein (CRP) as an inflammatory marker. Clinical data were extracted from the literature for anakinra, canakinumab, infliximab, secukinumab and tocilizumab, along with their corresponding cytokines, interleukin-1 receptor antagonist, IL-1β, tumour necrosis factor α (TNFα), IL-17A and IL-6 receptor (IL-6R). Based on prior knowledge of regulatory mechanisms, cytokines were integrated with CRP profiles. Key results: The model well captured all serum concentration-time profiles of cytokines and CRP ratios to respective baselines following drug treatment with good precision. On external validation, reasonable model performance on CRP dynamics, including rebound effects, was confirmed with clinical data not used in model development. Model-based simulations demonstrated that serum infliximab concentrations were accurately recapitulated in both a dose- and baseline TNFα-dependent manner. Furthermore, high baseline profiles of both IL-1β and/or targeted cytokines could be predictors of poor responses to biologics targeting TNFα and IL-6R, although the impact of IL-1β must be carefully interpreted. Conclusions and implication: Our model provides a quantitative platform to guide targeting and dosing strategies, including combination and/or sequential therapy, according to distinct baseline cytokine patterns in rheumatoid arthritis patients.
MeSH term(s)	Arthritis, Rheumatoid/drug therapy ; Biological Products/therapeutic use ; C-Reactive Protein ; Cytokines ; Humans ; Infliximab/therapeutic use ; Receptors, Interleukin-6 ; Tumor Necrosis Factor-alpha
Chemical Substances	Biological Products ; Cytokines ; Receptors, Interleukin-6 ; Tumor Necrosis Factor-alpha ; C-Reactive Protein (9007-41-4) ; Infliximab (B72HH48FLU)
Language	English
Publishing date	2022-04-22
Publishing country	England
Document type	Journal Article
ZDB-ID	80081-8
ISSN	1476-5381 ; 0007-1188
ISSN (online)	1476-5381
ISSN	0007-1188
DOI	10.1111/bph.15845
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Article ; Online: Network-Based Systems Analysis Explains Sequence-Dependent Synergism of Bortezomib and Vorinostat in Multiple Myeloma.

Nanavati, Charvi / Mager, Donald E

The AAPS journal

2021 Volume 23, Issue 5, Page(s) 101

Abstract: Bortezomib and vorinostat exhibit synergistic effects in multiple myeloma (MM) cells when given in sequence, and the purpose of this study was to evaluate the molecular determinants of the interaction using a systems pharmacology approach. A Boolean ... ...

Abstract	Bortezomib and vorinostat exhibit synergistic effects in multiple myeloma (MM) cells when given in sequence, and the purpose of this study was to evaluate the molecular determinants of the interaction using a systems pharmacology approach. A Boolean network model consisting of 79 proteins and 225 connections was developed using literature information characterizing mechanisms of drug action and intracellular signal transduction. Network visualization and structural analysis were conducted, and model simulations were compared with experimental data. Critical biomarkers, such as pNFκB, p53, cellular stress, and p21, were identified using measures of network centrality and model reduction. U266 cells were then exposed to bortezomib (3 nM) and vorinostat (2 μM) as single agents or in simultaneous and sequential (bortezomib for first 24 h, followed by addition of vorinostat for another 24 h) combinations. Temporal changes for nine of the critical proteins in the reduced Boolean model were measured over 48 h, and cellular proliferation was measured over 96 h. A mechanism-based systems model was developed that captured the biological basis of a bortezomib and vorinostat sequence-dependent pharmacodynamic interaction. The model was further extended in vivo by linking in vitro parameter values and dynamics of p21, caspase-3, and pAKT biomarkers to tumor growth in xenograft mice reported in the literature. Network-based methodologies and pharmacodynamic principles were integrated successfully to evaluate bortezomib and vorinostat interactions in a mechanistic and quantitative manner. The model can be potentially applied to evaluate their combination regimens and explore in vivo dosing regimens.
MeSH term(s)	Animals ; Antineoplastic Combined Chemotherapy Protocols/pharmacology ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Bortezomib/pharmacology ; Bortezomib/therapeutic use ; Cell Line, Tumor ; Cell Proliferation/drug effects ; Cell Proliferation/genetics ; Drug Synergism ; Female ; Humans ; Mice ; Models, Biological ; Multiple Myeloma/drug therapy ; Multiple Myeloma/genetics ; Network Pharmacology ; Protein Interaction Maps/drug effects ; Protein Interaction Maps/genetics ; Signal Transduction/drug effects ; Signal Transduction/genetics ; Systems Analysis ; Vorinostat/pharmacology ; Vorinostat/therapeutic use ; Xenograft Model Antitumor Assays
Chemical Substances	Vorinostat (58IFB293JI) ; Bortezomib (69G8BD63PP)
Language	English
Publishing date	2021-08-17
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1550-7416
ISSN (online)	1550-7416
DOI	10.1208/s12248-021-00622-9
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Article ; Online: Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma.

Chen, Ting / Zheng, Yanan / Roskos, Lorin / Mager, Donald E

Journal of pharmacokinetics and pharmacodynamics

2023 Volume 50, Issue 4, Page(s) 251–265

Abstract: Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a ... ...

Abstract	Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (k
MeSH term(s)	Humans ; Carcinoma, Transitional Cell/drug therapy ; Kinetics ; Urinary Bladder Neoplasms/drug therapy ; Antibodies, Monoclonal/therapeutic use
Chemical Substances	durvalumab (28X28X9OKV) ; Antibodies, Monoclonal
Language	English
Publishing date	2023-03-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041601-5
ISSN	1573-8744 ; 1567-567X
ISSN (online)	1573-8744
ISSN	1567-567X
DOI	10.1007/s10928-023-09848-w
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Article ; Online: Population-based meta-analysis of bortezomib exposure-response relationships in multiple myeloma patients.

Zhang, Li / Mager, Donald E

Journal of pharmacokinetics and pharmacodynamics

2020 Volume 47, Issue 1, Page(s) 77–90

Abstract: Bortezomib (Velcade®) is a reversible proteasome inhibitor that shows potent antineoplastic activity, by inhibiting the constitutively increased proteasome activity in myeloma cells, and is approved as a first-line therapy for multiple myeloma (MM). ... ...

Abstract	Bortezomib (Velcade®) is a reversible proteasome inhibitor that shows potent antineoplastic activity, by inhibiting the constitutively increased proteasome activity in myeloma cells, and is approved as a first-line therapy for multiple myeloma (MM). Although clinically successful, bortezomib exhibits a relatively narrow therapeutic index and can induce dose-limiting toxicities such as thrombocytopenia. This study aims to develop a quantitative and predictive pharmacodynamic model to investigate bortezomib dosing-regimens in a rational and efficient manner. Mean temporal profiles of bortezomib pharmacokinetics, proteasome activity, M-protein concentrations, and platelet counts following bortezomib monotherapy were extracted from published clinical studies. A population-based meta-analysis of bortezomib anti-myeloma activity and thrombocytopenia was conducted sequentially with a Stochastic Approximation Expectation Maximization algorithm in Monolix. The final pharmacodynamic model integrates drug-target interactions and cell signaling dynamics with temporal biomarkers of clinical efficacy and toxicity. Bortezomib pharmacokinetics, disease progression, and platelet dynamic profiles were well characterized in MM patients, and a local sensitivity analysis of the final model suggests that increased proteasome concentration could ultimately attenuate bortezomib antineoplastic activity in MM patients. In addition, model simulations confirm that a once-weekly dosing schedule represents an optimal therapeutic regimen with comparable antineoplastic activity but significantly reduced risk of thrombocytopenia. In conclusion, a pharmacodynamic model was successfully developed, which provides a quantitative, mechanism-based platform for probing bortezomib dosing-regimens. Further research is needed to determine whether this model could be used to individualize bortezomib regimens to maximize antineoplastic efficacy and minimize thrombocytopenia during MM treatment.
MeSH term(s)	Antineoplastic Agents/administration & dosage ; Antineoplastic Agents/adverse effects ; Biomarkers, Tumor/metabolism ; Bortezomib/administration & dosage ; Bortezomib/adverse effects ; Dose-Response Relationship, Drug ; Drug Administration Schedule ; Humans ; Multiple Myeloma/drug therapy ; Multiple Myeloma/metabolism ; Thrombocytopenia/chemically induced ; Thrombocytopenia/metabolism
Chemical Substances	Antineoplastic Agents ; Biomarkers, Tumor ; Bortezomib (69G8BD63PP)
Language	English
Publishing date	2020-01-14
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Research Support, N.I.H., Extramural
ZDB-ID	2041601-5
ISSN	1573-8744 ; 1567-567X
ISSN (online)	1573-8744
ISSN	1567-567X
DOI	10.1007/s10928-019-09670-3
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Article ; Online: Target Reserve and Turnover Parameters Determine Rightward Shift of Enalaprilat Potency From its Binding Affinity to the Angiotensin Converting Enzyme.

Nguyen, Van Anh / Zhang, Li / Kagan, Leonid / Rowland, Malcolm / Mager, Donald E

Journal of pharmaceutical sciences

2023 Volume 113, Issue 1, Page(s) 167–175

Abstract: ... enough to influence pharmacokinetics but insufficient to elicit a drug response (i.e., differences ...

Abstract	Drug effects are often assumed to be directly proportional to the fraction of occupied targets. However, for a number of antagonists that exhibit target-mediated drug disposition (TMDD), such as angiotensin-converting enzyme (ACE) inhibitors, drug binding to the target at low concentrations may be significant enough to influence pharmacokinetics but insufficient to elicit a drug response (i.e., differences in drug-target binding affinity and potency). In this study, a pharmacokinetic/pharmacodynamic model for enalaprilat was developed in humans to provide a theoretical framework for assessing the relationship between ex vivo drug potency (IC
MeSH term(s)	Humans ; Enalaprilat/pharmacology ; Peptidyl-Dipeptidase A/metabolism ; Angiotensin-Converting Enzyme Inhibitors/pharmacology ; Binding, Competitive
Chemical Substances	Enalaprilat (GV0O7ES0R3) ; Peptidyl-Dipeptidase A (EC 3.4.15.1) ; Angiotensin-Converting Enzyme Inhibitors
Language	English
Publishing date	2023-10-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	3151-3
ISSN	1520-6017 ; 0022-3549
ISSN (online)	1520-6017
ISSN	0022-3549
DOI	10.1016/j.xphs.2023.10.025
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Article: Editorial: Model-informed decision making in the preclinical stages of pharmaceutical research and development.

Li, Rui / Craig, Morgan / D'Argenio, David Z / Betts, Alison / Mager, Donald E / Maurer, Tristan S

Frontiers in pharmacology

2023 Volume 14, Page(s) 1184914

Language	English
Publishing date	2023-04-12
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2023.1184914
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Article ; Online: Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping.

Derippe, Thibaud / Fouliard, Sylvain / Declèves, Xavier / Mager, Donald E

Journal of pharmacokinetics and pharmacodynamics

2022 Volume 49, Issue 6, Page(s) 625–644

Abstract: ... in which the clinical phenotypes (i.e., treatment sensitive or resistant) of 200,000 VPs were fully characterized ...

Abstract	The generation of plausible virtual patients (VPs) is an important step in most quantitative systems pharmacology (QSP) workflows, which requires time-intensive solving of ordinary differential equations (ODEs). However, non-physiological profiles of outputs of interest (OoI) are frequently produced, and additional acceptance/rejection steps are needed for comparing and removing VPs with predicted values outside a pre-defined range. Here, a new approach is developed to accelerate the acceptance/rejection steps by leveraging patterns of parameter associations with OoI. In most models, some parameters are monotonic with respect to OoI, such that an increase in a parameter value always induces an increase or decrease in the OoI. This monotonic property can be used to replace some ODE-solving steps with appropriate monotonic parameter value comparisons to extrapolate the rejection or interpolate the acceptance of some VPs (after simulation) to others. Two algorithms were built that directly extract plausible VPs from a pre-defined initial cohort. These algorithms were first tested using a simple tumor growth inhibition model. Analyzing 200,000 VPs took 50 s with a reference method and 3 to 41 s (depending on the initial set-up) with the first algorithm. The method was then applied to an apoptosis QSP model, in which the clinical phenotypes (i.e., treatment sensitive or resistant) of 200,000 VPs were fully characterized for four different drug regimens in 12 min as compared to over 80 min with the reference approach. Extraction of each phenotype can also be performed individually in 34 s to 8 min, demonstrating the time benefit and flexibility of this approach.
MeSH term(s)	Computer Simulation ; Algorithms ; Cohort Studies ; Models, Theoretical
Language	English
Publishing date	2022-10-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2041601-5
ISSN	1573-8744 ; 1567-567X
ISSN (online)	1573-8744
ISSN	1567-567X
DOI	10.1007/s10928-022-09826-8
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Article ; Online: Integrated PK/PD Modeling Relates Smoothened Inhibitor Biomarkers to The Heterogeneous Intratumor Disposition of Cetuximab in Pancreatic Cancer Tumor Models.

Wang, Jun / Chen, Ting / Ruszaj, Donna M / Mager, Donald E / Straubinger, Robert M

Journal of pharmaceutical sciences

2023 Volume 113, Issue 1, Page(s) 72–84

Abstract: Therapeutic antibodies have shown little efficacy in the treatment of pancreatic ductal adenocarcinomas (PDAC). Tumor desmoplasia, hypovascularity, and poor perfusion result in insufficient tumor cell exposure, contributing to treatment failure. ... ...

Abstract	Therapeutic antibodies have shown little efficacy in the treatment of pancreatic ductal adenocarcinomas (PDAC). Tumor desmoplasia, hypovascularity, and poor perfusion result in insufficient tumor cell exposure, contributing to treatment failure. Smoothened inhibitors of hedgehog signaling (sHHi) increase PDAC tumor permeability, perfusion, and drug delivery, and provide a tool to develop a quantitative, mechanistic understanding as to how the temporal dynamics of tumor priming can impact intratumor distribution of monoclonal antibodies (mAb). A linked pharmacokinetic (PK)/pharmacodynamic (PD) model was developed to integrate the plasma and tumor PK of a sHHi priming agent with its effects upon downstream stromal biomarkers Gli1, hyaluronic acid, and interstitial fluid pressure in PDAC patient-derived xenograft (PDX) tumors. In parallel, in situ tumor concentrations of cetuximab (CTX: anti-epidermal growth factor receptor; EGFR) were quantified as a marker for tumor delivery of mAb or antibody-drug conjugates. A minimal, physiologically-based pharmacokinetic (mPBPK) model was constructed to link sHHi effects upon mechanistic effectors of tumor barrier compromise with the intratumor distribution of CTX, and CTX occupancy of EGFR in tumors. Integration of the mPBPK model of mAb deposition and intratumor distribution with the PK/PD model of tumor responses to priming not only identified physiological parameters that are critical for tumor antibody distribution, but also provides insight into dosing regimens that could achieve maximal tumor disposition of therapeutic antibodies under conditions of transient PDAC tumor permeability barrier compromise that mechanistically-diverse tumor priming strategies may achieve.
MeSH term(s)	Humans ; Cetuximab/therapeutic use ; Hedgehog Proteins/therapeutic use ; Pancreatic Neoplasms/drug therapy ; Pancreatic Neoplasms/pathology ; Drug Delivery Systems ; Carcinoma, Pancreatic Ductal/drug therapy ; Carcinoma, Pancreatic Ductal/pathology ; Antibodies, Monoclonal/pharmacokinetics ; ErbB Receptors
Chemical Substances	Cetuximab (PQX0D8J21J) ; Hedgehog Proteins ; Antibodies, Monoclonal ; ErbB Receptors (EC 2.7.10.1)
Language	English
Publishing date	2023-10-14
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	3151-3
ISSN	1520-6017 ; 0022-3549
ISSN (online)	1520-6017
ISSN	0022-3549
DOI	10.1016/j.xphs.2023.10.019
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Article ; Online: Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.

Bloomingdale, Peter / Mager, Donald E

Pharmaceutical research

2019 Volume 36, Issue 2, Page(s) 35

Abstract: Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the ... ...

Abstract	Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the identification of novel treatment strategies. Computational models of drug neurotoxicity could be implemented in early drug discovery to screen for high-risk compounds and select safer drug candidates for further development. Methods: Quantitative-structure toxicity relationship (QSTR) models were developed to predict the incidence of PN. A manually curated library of 95 approved drugs were used to develop the model. Molecular descriptors sensitive to the incidence of PN were identified to provide insights into structural modifications to reduce neurotoxicity. The incidence of PN was predicted for 60 antineoplastic drug candidates currently under clinical investigation. Results: The number of aromatic nitrogens was identified as the most important molecular descriptor. The chemical transformation of aromatic nitrogens to carbons reduced the predicted PN incidence of bortezomib from 32.3% to 21.1%. Antineoplastic drug candidates were categorized into three groups (high, medium, low) based on their predicted PN incidence. Conclusions: QSTR models were developed to link physicochemical descriptors of compounds with PN incidence, which can be utilized during drug candidate selection to reduce neurotoxicity.
MeSH term(s)	Antineoplastic Agents/adverse effects ; Antineoplastic Agents/chemistry ; Drug Design ; Humans ; Incidence ; Machine Learning ; Molecular Structure ; Neoplasms/drug therapy ; Neural Networks (Computer) ; Peripheral Nervous System Diseases/chemically induced ; Peripheral Nervous System Diseases/diagnosis ; Peripheral Nervous System Diseases/epidemiology ; Structure-Activity Relationship
Chemical Substances	Antineoplastic Agents
Language	English
Publishing date	2019-01-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	843063-9
ISSN	1573-904X ; 0724-8741 ; 0739-0742
ISSN (online)	1573-904X
ISSN	0724-8741 ; 0739-0742
DOI	10.1007/s11095-018-2562-7
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