Toward Dynamic Prescribing Information: Codevelopment of Companion Model‐Informed Precision Dosing Tools in Drug Development

Polasek, Thomas M.; Rayner, Craig R.; Peck, Richard; Rowland, Andrew; Kimko, Holly; Rostami‐Hodjegan, Amin

doi:10.1002/cpdd.638

Cited by 31 publications

(29 citation statements)

References 51 publications

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“…Though PGx and TDM are useful clinical tools for dose optimization, the optimal approach is to incorporate all relevant information about the drug’s PK/PD/PGx, using Model Informed Precision Dosing employs PKPD modeling and simulation (M&S) techniques in clinical care to optimize dose selection for an individual patient. This optimal dose is most likely to be associated with improved therapeutic outcomes, including optimal efficacy and minimal undesired events ( Darwich et al, 2017 , Polasek et al, 2018 , Tängdén et al, 2017 , Neely and Jelliffe, 2010 , Leroux et al, 2016 , Neely et al, 2016 , Neely et al, 2015 ). The capacity of model informed precision dosing to yield an optimal dose is due to the inherent ability of PKPD techniques to account for between-subject variability ( Collins and Varmus, 2015 ).…”

Section: Application In Clinical Carementioning

confidence: 99%

“…The implications of between-subject variability is noticed in clinical practice as one dose result in a range of drug concentrations and responses in a population ( Polasek et al, 2019 ). Between-subject variability is attributed to drug-related factors (such as drug-drug interactions and concurrent use of medications), PGx, and patient’s physiological and demographic factors (age, obesity, severity of diseases, and presence of comorbidities) ( Polasek et al, 2019 , Polasek et al, 2018 ).…”

Section: Application In Clinical Carementioning

confidence: 99%

“…To maintain simplicity and avoid confusion in clinical practice, these regimens are communicated in a simple static format and as a function of a limited number of predictive variables (e.g. nomograms, tables, and formulas) ( Polasek et al, 2018 , Jadhav et al, 2015 ). This oversimplified approach limits the amount of information available, forcing clinicians to rely on local guidance or make inferences ( in cerebro modeling) ( Darwich et al, 2017 , Jadhav et al, 2015 ) and discrepancies in dose adjustments may arise.…”

Section: Application In Clinical Carementioning

confidence: 99%

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Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Alsultan

Alghamdi

et al. 2020

Saudi Pharmaceutical Journal

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Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

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Section: Application In Clinical Carementioning

confidence: 99%

Section: Application In Clinical Carementioning

confidence: 99%

Section: Application In Clinical Carementioning

confidence: 99%

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Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Alsultan

Alghamdi

et al. 2020

Saudi Pharmaceutical Journal

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“…These efforts and ongoing changes in our approach to labeling of medicines are important short‐term improvements. However, realizing the potential of truly individualized patient care and dosing will require larger transformative changes across the broader ecosystem spanning drug development, regulation, and utilization, as has been advocated in recent perspectives …”

Section: Path Forwardmentioning

confidence: 99%

Come Dance With Me: Transformative Changes in the Science and Practice of Drug–Drug Interactions

Venkatakrishnan

Rostami‐Hodjegan

2019

Clin Pharma and Therapeutics

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The past 2 decades have witnessed transformative changes in our approach to drug-drug interactions (DDIs) from scientific research to clinical practice. Collaborative cooperation across the sectors of academia, pharmaceutical industry, providers of translational research tools and services, global regulatory agencies, and healthcare providers has created and galvanized a science-informed and patientcentered approach. Multidisciplinary innovations in mechanistic assessment of absorption, distribution, metabolism, and excretion (ADME), population pharmacology and pharmacogenetics, physiologically based modeling, and regulatory science have enabled a profound shift in mindset from risk aversion to informative prescribing guidance for optimal risk management. Foundational to this transformation is a commitment to maximizing the value of innovative therapeutics for all patients and across clinical contexts of use through rational knowledge management and translation based on the totality of best available evidence, thereby removing the void that can lead to arbitrary decisions in clinical therapeutics.

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“…The primary objective of this study was to identify physiological and molecular characteristics driving variability in axitinib AUC SS using physiologically based pharmacokinetic (PBPK) modeling. Identification of these characteristics informs analyses of “exposure biomarkers” for axitinib that can be evaluated using routinely collected samples from randomized controlled trials, and will facilitate noninvasive precision dosing for this drug . The second objective of this study was to determine the predictive performance of the identified characteristics with respect to defining axitinib exposure by determining the capacity of a multivariable logistic regression model to identify patients most likely to fail to achieve an axitinib AUC SS above 300 ng/mL/hr with standard (5 mg twice daily) dosing.…”

mentioning

confidence: 99%

Use of Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Axitinib Exposure: A Fresh Approach to Precision Dosing in Oncology

Sorich

Mutlib

Dyk

et al. 2019

The Journal of Clinical Pharma

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Axitinib is a second‐generation small‐molecule vascular endothelial growth factor receptor inhibitor. An axitinib steady‐state area under the plasma concentration–time curve (AUCSS) >300 ng/mL/hr is associated with superior progression‐free and overall survival. This study sought to characterize the physiological and molecular characteristics driving variability in axitinib AUCSS using physiologically based pharmacokinetic modeling to identify exposure biomarkers for this drug. The capacity to predict subjects likely to fail to achieve an axitinib AUCSS >300 ng/mL/hr was evaluated as a secondary outcome. A full physiologically based pharmacokinetic model incorporating mechanistic absorption was developed and verified for axitinib in accordance with the US Food and Drug Administration Guidance using Simcyp (Version 17.1). This model was used to simulate axitinib exposure over 7 days with twice‐daily dosing (5 mg) in a cohort of 1000 virtual cancer patients. Multiple linear regression modeling was used to identify patient characteristics associated with differences in axitinib exposure. A multivariable linear regression model incorporating hepatic cytochrome P450 (CYP) 3A4 abundance, albumin concentration, hepatic CYP1A2 abundance, hepatic CYP2C19 abundance, and intestinal CYP2C19 abundance provided robust prediction of axitinib AUCSS (R2 = 0.890; P < .001). By accounting for these variables, it was possible to identify subjects who would fail to achieve an effective axitinib AUCSS with a specificity of 88.7% and a sensitivity of 92.6%. Variability in axitinib AUCSS is primarily driven by differences in hepatic CYP3A4 abundance and albumin concentration. Consideration of these 2 characteristic is likely to be sufficient to individualize axitinib dosing.

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Toward Dynamic Prescribing Information: Codevelopment of Companion Model‐Informed Precision Dosing Tools in Drug Development

Cited by 31 publications

References 51 publications

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Come Dance With Me: Transformative Changes in the Science and Practice of Drug–Drug Interactions

Use of Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Axitinib Exposure: A Fresh Approach to Precision Dosing in Oncology

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