Drug Delivery Approaches

Perspectives from Pharmacokinetics and Pharmacodynamics

Bret Berner, Toufigh Gordi, Heather A. E. Benson, Michael S. Roberts, Bret Berner, Toufigh Gordi, Heather A. E. Benson, Michael S. Roberts

• Publisher: Wiley
• Year: 2021
• ISBN:
  9781119772750
• Edition: 1
• Topic: Médecine
• Subtopic: Pharmacologie

Explore this comprehensive discussion of the application of physiologically- and physicochemical-based models to guide drug delivery edited by leading experts in the field

Drug Delivery Approaches: Perspectives from Pharmacokinetics and Pharmacodynamics delivers a thorough discussion of drug delivery options to achieve target profiles and approaches as defined by physical and pharmacokinetic models. The book offers an overview of drug absorption and physiological models, chapters on oral delivery routes with a focus on both PBPK and multiple dosage form options. It also provides an explanation of the pharmacokinetics of the formulation of drugs delivered by systemic transdermal routes.

The distinguished editors have included practical and accessible resources that address the biological and delivery approaches to pulmonary and mucosal delivery of drugs. Emergency care settings are also described, with explorations of the relationship between parenteral infusion profiles and PK/PD. The future of drug delivery is addressed via discussions of virtual experiments to elucidate mechanisms and approaches to drug delivery and personalized medicine.

Readers will also benefit from the inclusion of:

• A thorough introduction to the utility of mathematical models in drug development and delivery
• An exploration of the techniques and applications of physiologically based models to drug delivery
• Discussions of oral delivery and pharmacokinetic models and oral site-directed delivery
• A review of integrated transdermal delivery and pharmacokinetics in development
• An examination of virtual experiment methods for integrating pharmacokinetic, pharmacodynamic, and drug delivery mechanisms
• Alternative endpoints to pharmacokinetics for topical delivery

Perfect for researchers, industrial scientists, graduate students, and postdoctoral students in the area of pharmaceutical science and engineering, Drug Delivery Approaches: Perspectives from Pharmacokinetics and Pharmacodynamics will also earn a place in the libraries of formulators, pharmacokineticists, and clinical pharmacologists.

1
Introduction: Utility of Mathematical Models in Drug Development and Delivery

Toufigh Gordi¹ and Bret Berner²

¹Sr. Director of Clinical Pharmacology at Rigel Therapeutics, South San Francisco, CA, USA

²Formulation and Drug Delivery Consultant, 4011 54 Ave SW, Seattle, WA, USA

1.1 Introduction

Models, particularly mathematical models, have been widely used to understand different aspects of human lives for a long period of time. Mathematical models not only can help explain observations, but also aid in predicting outcomes of experiments. Models are frequently used in our daily lives. As an example, consider the following equation as a model:

Knowing the relationship between distance, speed, and time, we use the model to predict the time to reach our destination on a trip. Furthermore, we plan our daily lives by predicting the time it takes for a trip or a simple question of whether we will be on time for a meeting. For this well‐established model, instead of performing the experiment of driving from point A to B to know how long it may take, we can have a good estimate of the time by knowing the distance of the destination is from us and how fast we expect to travel. Models can simplify our experiments or even alleviate them. The powerful ability of models to predict outcomes of unknown experiments makes them an essential part of a wide range of modern industries, from aviation to auto to computer and financial entities. New airplane designs are tested less than a handful times in the air before large‐scale production starts and they are in regular use. New car crash tests are performed in computer simulations before an actual prototype is crash‐tested a few times. Models help advance product development at a faster rate and a fraction of the cost associated with actual experiments. It is not surprising that the use of mathematical models has gained widespread attention within the pharmaceutical industry and with regulators. With drug development being a lengthy and enormously expensive process, mathematical models have the potential of optimizing a therapeutic regimen, decreasing costs, and shortening the timelines significantly.

In this chapter, we describe a few mathematical models and their utility in drug development and provide examples of the use of these models in predicting clinical study outcomes or indicating possible causes of the observations, thereby helping researchers focus on areas that have the most impact on the development path of a drug. Within physiologically‐ based pharmacokinetic (PBPK) in regulatory submissions, only a small fraction, quoted as 6% of PBPK models, focused on drug delivery [1], whether it be food effects or issues in drug absorption. In some exceptional cases, the verification of the PBPK model was sufficient to avoid bioequivalence studies. In this chapter, we focus mostly on models of the drug delivery itself to design or select the target delivery profile or the dosage form itself. Other chapters throughout this book will provide more detailed descriptions of the principles of developing mathematical models in the drug‐development process and for the different routes of drug delivery.

1.2 Use of Mathematical Models in Drug Development

The relationship between dose and effect of a remedy must have been known to ancient healers, who often used mixtures of different herbs and other organic material at certain proportions to combat diseases. Despite significant changes to pharmacological intervention aimed at mitigation of symptoms and curing various diseases, drug‐development goals today are strikingly similar: find the dose of a compound that provides the maximum desired effect while avoiding unwanted side effects. This central question of what dose (amount, frequency) is a major reason for late‐phase, confirmatory clinical development programs [2]. In early stages of the modern pharmaceutical industry, the focus was on establishing a dose–response relationship. With advances in analytical chemistry to allow measurement of drug concentrations in blood, plasma, urine, or other body fluids, the focus has shifted to elucidating a concentration–response, or in broader form, dose–concentration–response relationships. This shift is logical as the larger observed variability in and the complexity of a dose–response relationship is reduced when investigating the concentration–response. Furthermore, concentration measurements offer the possibility to follow the temporal changes of drug concentrations, thereby providing a tool to understand the time course of the pharmacological effect of a compound. Mathematical models that could describe the time course of drug concentrations were first proposed in late 1930s [3,4]. However, due to the complexity of models required and the lack of computers, only models solved by analytical solutions could be developed. The perceived complex underlying mathematics resulted in the utility of mathematical models not being appreciated by the medical community. Introduction of physiologically related parameters such as clearance during 1970s made it easier for the medical community to understand and appreciate these models by relating them to the biology of the disease and the pharmacology of the treatment [5]. Furthermore, academic centers contributed to the advancement of general concepts of using mathematical models to describe the time course of drug concentrations in the body (pharmacokinetics, PK) and its effects (pharmacodynamics, PD) [6–8].

Mathematical models have two main functions: describing observations and predicting untested scenarios using simulations. Thus, models are modified when new experimental data becomes available. that are not consistent with the original model. The process of model development involves building simple models based on limited available data. This simple model is then used to guide the design of future studies. Once a new study is performed, the generated data are compared to the simulations by the model to test its validity. If the new data agree with the predictions of the model, the experimental data can be added to the data pool and re‐estimate the model parameters using a larger data set. On the other hand, if the model was unable to predict the new data, the underlying assumptions or the model may be modified to include new insights gained through the new experiment. This cycle of learning and testing is an essential part of the model development process and is routinely used in drug development [9,10]. Early development programs of a new drug can be more efficient by developing and enhancing the relevant models, allowing for more accurate and reliable design and predict...