Virtual Twins: Understanding the Data Required for Model‐Informed Precision Dosing

Polasek, Thomas M.; Rostami‐Hodjegan, Amin

doi:10.1002/cpt.1778

Cited by 44 publications

(46 citation statements)

References 10 publications

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“…PBPK modelling and simulation is an established tool to support drug discovery and development, and is a core element of the regulatory approval process in many jurisdictions [ 35 ]. Recent studies have further demonstrated the potential role of PBPK in predicting covariates affecting variability in drug exposure resulting from either patient characteristics or the drugs’ physicochemical properties [ 24 , 25 ], giving rise to the intriguing potential for this platform to support model informed precision dosing [ 26 , 32 ]. Since the introduction of imatinib in 2001 there has been a growing evidence base supporting a role for concentration-guided KI dosing, despite this implementation of KI dose individualisation has remained challenging.…”

Section: Discussionmentioning

confidence: 99%

“…Model-informed initial dose selection (MIDS), often underpinned by a population pharmacokinetic (pop-PK) or physiologically based pharmacokinetic (PBPK) model, has emerged as a strategy to assist initial dose selection either to complement or replace TDM [ 24 , 25 , 26 , 27 ]. PBPK modelling and simulation is an established tool in drug discovery and development, where it is used to predict factors affecting PK and support the design of clinical trials [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing

et al. 2021

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The primary objective of this study is to evaluate the capacity of concentration-guided sorafenib dosing protocols to increase the proportion of patients that achieve a sorafenib maximal concentration (Cmax) within the range 4.78 to 5.78 μg/mL. A full physiologically based pharmacokinetic model was built and validated using Simcyp® (version 19.1). The model was used to simulate sorafenib exposure in 1000 Sim-Cancer subjects over 14 days. The capacity of concentration-guided sorafenib dose adjustment, with/without model-informed dose selection (MIDS), to achieve a sorafenib Cmax within the range 4.78 to 5.78 μg/mL was evaluated in 500 Sim-Cancer subjects. A multivariable linear regression model incorporating hepatic cytochrome P450 (CYP) 3A4 abundance, albumin concentration, body mass index, body surface area, sex and weight provided robust prediction of steady-state sorafenib Cmax (R2 = 0.883; p < 0.001). These covariates identified subjects at risk of failing to achieve a sorafenib Cmax ≥ 4.78 μg/mL with 95.0% specificity and 95.2% sensitivity. Concentration-guided sorafenib dosing with MIDS achieved a sorafenib Cmax within the range 4.78 to 5.78 μg/mL for 38 of 52 patients who failed to achieve a Cmax ≥ 4.78 μg/mL with standard dosing. In a simulation setting, concentration-guided dosing with MIDS was the quickest and most effective approach to achieve a sorafenib Cmax within a designated range.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing

et al. 2021

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“…We also seek to describe the degree of adoption of MIDD practices across the field and future applications. This review is constrained to MIDD activities of lead candidates through to regulatory and payer approval and does not include MIDD as it pertains to drug discovery or precision dosing, such as therapeutic drug monitoring, application of virtual twin approaches using PBPK, 16 or dual individualization principles where PKs, pathogen sensitivity, and PKs/PDs are integrated to guide dosing decisions for individual patients 17 …”

Section: Figurementioning

confidence: 99%

Model‐Informed Drug Development for Anti‐Infectives: State of the Art and Future

Rayner

Smith

Andes

et al. 2021

Clin Pharma and Therapeutics

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Model‐informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti‐infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug‐drug interactions. Examples are presented for state‐of‐the‐art, empiric, mechanistic, interdisciplinary, and real‐world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration‐based models, mechanism‐based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; “depth” and “breadth” of MIDD methods. “MIDD depth” refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired‐resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. “MIDD breadth” refers to greater adoption of model‐centered approaches to anti‐infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

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“…They concluded that although the clinical data fall within confidence intervals of the predictions and trends are instructive, the model overpredicted efficacy. Nevertheless, this case study constitutes an important demonstration of a "virtual twin" 66 methodology, where each VP represents an actual clinical subjects for whom sufficient data are available to instantiate the model and predict an individual tumor trajectory. In another case study on breast cancer, Wang et al 65 presented application of their QSP model to combination of immunotherapy with another class of drugsepigenetic inhibitors.…”

Section: Reviewmentioning

confidence: 99%

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Chelliah¹,

Lazarou²,

Bhatnagar

et al. 2020

Clin Pharma and Therapeutics

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Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

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Virtual Twins: Understanding the Data Required for Model‐Informed Precision Dosing

Cited by 44 publications

References 10 publications

Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing

Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing

Model‐Informed Drug Development for Anti‐Infectives: State of the Art and Future

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

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