Model-based Dose Individualization Approaches Using Biomarkers

Objectives: This presentation aims at describing how modeling framework incorporating biomarker data can be a valuable tool for dose individualization in various diseases. Information on specific models of drugs such as sunitinib, warfarin, sitagliptin, etc. with corresponding biomarker data will be discussed in detail. Overview/Description of presentation: Biomarkers are helpful in clinical practice as a diagnostic tool, surrogate endpoint to assess clinical safety and efficacy, and for dose individualization. By incorporating complete time-course of biomarker changes in a model, we can quantitatively characterize the link between exposure, biomarker concentrations, and clinical outcome. An established relationship, therefore, may be used for prediction of changes in biomarker concentration and the resulting clinical outcome under a variety of conditions to evaluate individualized dosing approaches. Several examples are available on how model-based analyses of biomarker data can support the dose individualization approach in various disease states. A model relating exposure of anticancer drug sunitinib, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)‐2, ‐3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was developed to be used for dose individualization to maximize OS [1]. A KPD model that describes the relationship between warfarin dose and international normalized ratio (INR) response was developed. The model can be used to manage a priori and a posteriori individualization of warfarin therapy in both adults and children [2]. Prostaglandin E2 (PGE2) levels and thromboxane A2 (TXA2) inhibition were utilized as biomarkers for developing a model to predict drug effects and select efficacious doses in humans [3]. The key steps in the development of a model incorporating biomarkers are: 1) Development of a population model that describes the PKPD relationship of the drug and identify and quantify important predictors for a priori dose individualization 2) Transfer the model to a user-friendly decision support tool for a priori and a posteriori predictions of drug dose and biomarkers response 3) Optimize performance of model using clinical data. Conclusions/Take home message: The models discussed in the presentation serve as examples of how pharmacometrics can be used to assess exposure-biomarker-adverse effects-and clinical outcomes relationship in an integrated manner. These models also provide suitable platforms for dose individualization approaches due to their ability to predict clinical outcomes based on biomarker information

Model-based Dose Individualization Approaches Using Biomarkers

Objectives: This presentation aims at describing how modeling framework incorporating biomarker data can be a valuable tool for dose individualization in various diseases. Information on specific models of drugs such as sunitinib, warfarin, sitagliptin, etc. with corresponding biomarker data will be discussed in detail. Overview/Description of presentation: Biomarkers are helpful in clinical practice as a diagnostic tool, surrogate endpoint to assess clinical safety and efficacy, and for dose individualization. By incorporating complete time-course of biomarker changes in a model, we can quantitatively characterize the link between exposure, biomarker concentrations, and clinical outcome. An established relationship, therefore, may be used for prediction of changes in biomarker concentration and the resulting clinical outcome under a variety of conditions to evaluate individualized dosing approaches. Several examples are available on how model-based analyses of biomarker data can support the dose individualization approach in various disease states. A model relating exposure of anticancer drug sunitinib, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)‐2, ‐3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was developed to be used for dose individualization to maximize OS [1]. A KPD model that describes the relationship between warfarin dose and international normalized ratio (INR) response was developed. The model can be used to manage a priori and a posteriori individualization of warfarin therapy in both adults and children [2]. Prostaglandin E2 (PGE2) levels and thromboxane A2 (TXA2) inhibition were utilized as biomarkers for developing a model to predict drug effects and select efficacious doses in humans [3]. The key steps in the development of a model incorporating biomarkers are: 1) Development of a population model that describes the PKPD relationship of the drug and identify and quantify important predictors for a priori dose individualization 2) Transfer the model to a user-friendly decision support tool for a priori and a posteriori predictions of drug dose and biomarkers response 3) Optimize performance of model using clinical data. Conclusions/Take home message: The models discussed in the presentation serve as examples of how pharmacometrics can be used to assess exposure-biomarker-adverse effects-and clinical outcomes relationship in an integrated manner. These models also provide suitable platforms for dose individualization approaches due to their ability to predict clinical outcomes based on biomarker information

Clinical Pharmacokinetics of Inhaled Antimicrobials

Administration of inhaled antimicrobials affords the ability to achieve targeted drug delivery into the respiratory tract, rapid entry into the systemic circulation, high bioavailability and minimal metabolism. These unique pharmacokinetic characteristics make inhaled antimicrobial delivery attractive for the treatment of many pulmonary diseases. This review examines recent pharmacokinetic trials with inhaled antibacterials, antivirals and antifungals, with an emphasis on the clinical implications of these studies. The majority of these studies revealed evidence of high antimicrobial concentrations in the airway with limited systemic exposure, thereby reducing the risk of toxicity. Sputum pharmacokinetics varied widely, which makes it challenging to interpret the result of sputum pharmacokinetic studies. Many no vel inhaled antimicrobial therapies are currently under investigation that will require detailed pharmacokinetic studies, including combination inhaled antimicrobial therapies, inhaled nanoparticle formulations of several antibacterials, inhaled non-antimicrobial adjuvants, inhaled antiviral recombinant protein therapies and semi-synthetic inhaled antifungal agents. Additionally, the development of new inhaled delivery devices, particularly for mechanically ventilated patients, will result in a pressing need for additional pharmacokinetic studies to identify optimal dosing regimens

Clinical Pharmacokinetics of Inhaled Antimicrobials

Administration of inhaled antimicrobials affords the ability to achieve targeted drug delivery into the respiratory tract, rapid entry into the systemic circulation, high bioavailability and minimal metabolism. These unique pharmacokinetic characteristics make inhaled antimicrobial delivery attractive for the treatment of many pulmonary diseases. This review examines recent pharmacokinetic trials with inhaled antibacterials, antivirals and antifungals, with an emphasis on the clinical implications of these studies. The majority of these studies revealed evidence of high antimicrobial concentrations in the airway with limited systemic exposure, thereby reducing the risk of toxicity. Sputum pharmacokinetics varied widely, which makes it challenging to interpret the result of sputum pharmacokinetic studies. Many no vel inhaled antimicrobial therapies are currently under investigation that will require detailed pharmacokinetic studies, including combination inhaled antimicrobial therapies, inhaled nanoparticle formulations of several antibacterials, inhaled non-antimicrobial adjuvants, inhaled antiviral recombinant protein therapies and semi-synthetic inhaled antifungal agents. Additionally, the development of new inhaled delivery devices, particularly for mechanically ventilated patients, will result in a pressing need for additional pharmacokinetic studies to identify optimal dosing regimens

The Relationship Between Pharmacogenomics and Pharmacokinetics and Its Impact on Drug Choice and Dosing Regimens in Pediatrics

The concept of precision or personalized medicine in pediatrics is still in its infancy, and due to ethical and logistical constraints, it is difficult to conduct clinical studies in pediatric to obtain meaningful correlations between ontogeny and drug disposition. However, as a result of initiatives by the Food and Drug Administration (FDA) aimed toward incentivizing companies for conducting pediatric trials, knowledge on pediatric pharmacogenomics is slowly increasing. The information on pediatric pharmacogenomics is utilized to implement pharmacogenomic testing in pediatrics to allow clinicians to make an informed decision on selection and dosing of drugs in pediatrics. The ontogeny of drug-metabolizing enzymes (DMEs), transporters, and target proteins is the most crucial factor in pediatric pharmacogenomics. Based on in vitro and in vivo studies on the ontogeny of DMEs, various pharmacogenomic tests in pediatrics were evaluated concerning the pharmacokinetics of drugs utilized in pediatric pharmacotherapy. Needing to obtain clinically relevant advantages of incorporating pharmacogenomics in pediatric drug therapy, clinicians must be informed on pharmacogenomic terms by appropriate educational programs. Furthermore, a comprehensive database that can bank all pediatric pharmacogenomic information that can seamlessly collaborate with other international databases must be established

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug–drug interactions, real-time assessment of pharmacokinetic–safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug–drug interactions, real-time assessment of pharmacokinetic–safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established