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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A comprehensive introduction to using modeling and simulation programs in drug discovery and development Biopharmaceutical modeling has become integral to the design and development of new drugs. Influencing key aspects of the development process, including drug substance design, formulation design, and toxicological exposure assessment, biopharmaceutical modeling is now seen as the linchpin to a drug's future success. And while there are a number of commercially available software programs for drug modeling, there has not been a single resource guiding pharmaceutical professionals to the actual tools and practices needed to design and test safe drugs. A guide to the basics of modeling and simulation programs, Biopharmaceutics Modeling and Simulations offers pharmaceutical scientists the keys to understanding how they work and are applied in creating drugs with desired medicinal properties. Beginning with a focus on the oral absorption of drugs, the book discusses: * The central dogma of oral drug absorption (the interplay of dissolution, solubility, and permeability of a drug), which forms the basis of the biopharmaceutical classification system (BCS) * The concept of drug concentration * How to simulate key drug absorption processes * The physiological and drug property data used for biopharmaceutical modeling * Reliable practices for reporting results With over 200 figures and illustrations and a peerless examination of all the key aspects of drug research--including running and interpreting models, validation, and compound and formulation selection--this reference seamlessly brings together the proven practical approaches essential to developing the safe and effective medicines of tomorrow.
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Seitenzahl: 674
Veröffentlichungsjahr: 2012
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Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved
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Library of Congress Cataloging-in-Publication Data:
Sugano, Kiyohiko. Biopharmaceutics modeling and simulations : theory, practice, methods,and applications / Kiyohiko Sugano. p. ; cm. Includes bibliographical references and index. ISBN 978-1-118-02868-1 (cloth) I. Title. [DNLM: 1. Biopharmaceutics–methods. 2. Computer Simulation. 3. Drug Compounding–methods. 4. Models, Theoretical. QV 38]
615.7–dc23
2012007296
To Hitomi
Table of Contents
Title
Copyright
Dedication
Preface
List of Abbreviations
Chapter 1: Introduction
1.1 An Illustrative Description of Oral Drug Absorption: The Whole Story
1.2 Three Regimes of Oral Drug Absorption
1.3 Physiology of the Stomach, Small Intestine, and Colon
1.4 Drug and API Form
1.5 The Concept of Mechanistic Modeling
References
Chapter 2: Theoretical Framework I: Solubility
2.1 Definition OF Concentration
2.2 Acid–Base and Bile-Micelle-Binding Equilibriums
2.3 Equilibrium Solubility
References
Chapter 3: Theoretical Framework II: Dissolution
3.1 Diffusion Coefficient
3.2 Dissolution and Particle Growth
3.3 Nucleation
References
Chapter 4: Theoretical Framework III: Biological Membrane Permeation
4.1 Overall Scheme
4.2 General Permeation Equation
4.3 Permeation Rate Constant, Permeation Clearance, and Permeability
4.4 Intestinal Tube Flatness and Permeation Parameters
4.5 Effective Concentration for Intestinal Membrane Permeability
4.6 Surface Area Expansion by Plicate and Villi
4.7 Unstirred Water Layer Permeability
4.8 Epithelial Membrane Permeability (Passive Processes)
4.9 Enteric Cell Model
4.10 Gut Wall Metabolism
4.11 Hepatic Metabolism and Excretion
References
Chapter 5: Theoretical Framework IV: Gastrointestinal Transit Models and Integration
5.1 GI Transit Models
5.2 Time-Dependent Changes of Physiological Parameters
5.3 Integration 1: Analytical Solutions
5.4 Integration 2: Numerical Integration
5.5 In vivo FA From PK Data
5.6 Other Administration Routes
References
Chapter 6: Physiology of Gastrointestinal Tract and other Administration Sites in Humans and Animals
6.1 Morphology of Gastrointestinal Tract
6.2 Movement of the Gastrointestinal Tract
6.3 Fluid Character of the Gastrointestinal Tract
6.4 Transporters and Drug-Metabolizing Enzymes in the Intestine
6.5 Intestinal and Liver Blood Flow
6.6 Physiology Related to Enterohepatic Recirculation
6.7 Nasal
6.8 Pulmonary
6.9 Skin
References
Chapter 7: Drug Parameters
7.1 Dissociation Constant (pKa)
7.2 Octanol–Water Partition Coefficient
7.3 Bile-Micelle Partition Coefficient (KBM)
7.4 Particle Size and Shape
7.5 Solid Form
7.6 Solubility
7.7 Dissolution Rate/Release Rate
7.8 Precipitation
7.9 Epithelial Membrane Permeability
7.10 In vivo Experiments
References
Chapter 8: Validation of Mechanistic Models
8.1 Concerns Related to Model Validation Using In Vivo Data
8.2 Strategy for Transparent and Robust Validation of Biopharmaceutical Modeling
8.3 Prediction Steps
8.4 Validation for Permeability-Limited Cases
8.5 Validation for Dissolution-Rate and Solubility-Permeability-Limited Cases (without the Stomach Effect)
8.6 Validation for Dissolution-Rate and Solubility-Permeability-Limited Cases (with the Stomach Effect)
8.7 Salts
8.8 Reliability of Biopharmaceutical Modeling
References
Chapter 9: Bioequivalence and Biopharmaceutical Classification System
9.1 Bioequivalence
9.2 The History of BCS
9.3 Regulatory Biowaiver Scheme and BCS
9.4 Exploratory BCS
9.5 In Vitro–In Vivo Correlation
References
Chapter 10: Dose and Particle Size Dependency
10.1 Definitions and Causes of Dose Nonproportionality
10.2 Estimation of the Dose and Particle Size Effects
10.3 Effect of Transporters
10.4 Analysis of InVivo Data
References
Chapter 11: Enabling Formulations
11.1 Salts and Cocrystals: Supersaturating API
11.2 Nanomilled API Particles
11.3 Self-Emulsifying Drug Delivery Systems (Micelle/Emulsion Solubilization)
11.4 Solid Dispersion
11.5 Supersaturable Formulations
11.6 Prodrugs to Increase Solubility
11.7 Prodrugs to Increase Permeability
11.8 Controlled Release
11.9 Communication with Therapeutic Project Team
References
Chapter 12: Food Effect
12.1 Physiological Changes Caused by Food
12.2 Types of Food Effects and Relevant Parameters in Biopharmaceutical Modeling
12.3 Effect of Food Type
12.4 Biopharmaceutical Modeling of Food Effect
References
Chapter 13: Biopharmaceutical Modeling for Miscellaneous Cases
13.1 Stomach pH Effect on Solubility and Dissolution Rate
13.2 Intestinal First-Pass Metabolism
13.3 Transit Time Effect
13.4 Other Chemical and Physical Drug–Drug Interactions
13.5 Species Difference
13.6 Validation of GI Site-Specific Absorption Models
References
Chapter 14: Intestinal Transporters
14.1 Apical Influx Transporters
14.2 Efflux Transporters
14.3 Dual Substrates
14.4 Difficulties In Simulating Carrier-Mediated Transport
14.5 Summary
References
Chapter 15: Strategy in Drug Discovery and Development
15.1 Library Design
15.2 Lead Optimization
15.3 Compound Selection
15.4 API Form Selection
15.5 Formulation Selection
15.6 Strategy to Predict Human Fa%
References
Chapter 16: Epistemology of Biopharmaceutical Modeling and Good Simulation Practice
16.1 Can Simulation be so Perfect?
16.2 Parameter Fitting
16.3 Good Simulation Practice
Appendix A: General Terminology
A.1 Biopharmaceutic
A.2 Bioavailability (BA% or F)
A.3 Drug Disposition
A.4 Fraction of a Dose Absorbed (Fa)
A.5 Modeling/Simulation/In Silico
A.6 Active Pharmaceutical Ingredient (API)
A.7 Drug Product
A.8 Lipophilicity
A.9 Acid and Base
A.10 Solubility
A.11 Molecular Weight (MW)
A.12 Permeability of a Drug
Appendix B: Fluid Dynamics
B.1 Navier–Stokes Equation and Reynolds Number
B.2 Boundary Layer Approximation
B.3 The Boundary Layer and Mass Transfer
B.4 The Thickness of the Boundary Layer
B.5 Sherwood Number
B.6 Turbulence
B.7 Formation of Eddies
B.8 Computational Fluid Dynamics
References
Index
Preface
“Science is built of facts the way a house is built of bricks; but an accumulation of facts is no more science than a pile of bricks is a house.”
—Henry Poincare
The aim of this book is to provide a systematic understanding of biopharmaceutical modeling. Probably, this is the first book challenging this difficult task.
Biopharmaceutical modeling demands a wide range of knowledge. We need to understand the physical theories, the physiology of the gastrointestinal tract, and the meaning of drug parameters. This book covers the wide range of scientific topics required to appropriately perform and evaluate biopharmaceutical modeling. In this book, oral absorption of a drug is mainly discussed. However, the same scientific framework is applicable for other administration routes such as nasal and pulmonary administrations.
Oral absorption of a drug is a complex process that consists of dissolution, precipitation, intestinal wall permeation, and gastrointestinal transit. In addition, drug metabolism can also occur in the intestinal wall and the liver before drug molecules enter into systemic circulation.
Historically, a reductionist approach has been taken to understand the oral absorption of a drug. Each process of oral absorption was reduced to its subprocesses up to the molecular level. However, understanding each piece of the puzzle is insufficient in understanding the whole picture of oral absorption. It is critically important to reconstruct the whole process of oral absorption and understand the interrelationship between each piece that comprises oral absorption of a drug.
In the field of biology, computational systems biology has been emerging since the millennium [1]. In systems biology, the interactions between biological molecules are investigated in both reductionist and constitutive approaches to understand the quantitative relationship between a disease state and each molecular process. In this book, a similar approach is applied for the oral absorption of a drug.
In the first section of this book, the whole picture of oral absorption is discussed. As the central dogma of oral drug absorption, the interplay of dissolution rate, solubility, and permeability of a drug is discussed in a comprehensive manner without using mathematics. Even though the discussion in the first section is only a conceptual and qualitative outline, correct understanding of this central dogma will be of great benefit for drug discovery and development. The central dogma of oral drug absorption is the basis of the biopharmaceutical classification system (BCS), which is widely used in drug discovery and development [2].
We then move forward to each theory that comprises the entire oral absorption model. In this book, the entire mathematical framework is called the “gastrointestinal unified theoretical framework (GUT framework).” The concept of “concentration” is first discussed in detail, as it is critically important for understanding biopharmaceutical modeling. Then, theories of solubility, dissolution, precipitation, membrane permeation, and drug metabolisms are discussed. Each theory is described based on the unified definition of drug concentration and then incorporated into the GUT framework.
We then move forward to the physiological and drug property data that is used for biopharmaceutical modeling. The quality of biopharmaceutical modeling heavily relies on the quality of input data. The input data are roughly categorized into drug property and physiological parameters. These data are reviewed from the viewpoint of their use in biopharmaceutical modeling.
Before moving on to the discussions about practical applications of biopharmaceutical modeling in drug research, the validity of biopharmaceutical modeling is critically reviewed. A step-by-step approach has been taken to validate the biopharmaceutical modeling employing Occam's razor as a leading principle.
As the applications of biopharmaceutical modeling in drug research, biopharmaceutical classification system, dose/particle size dependency prediction, selection of solid form and enabling formulation, food effect prediction, etc. are then discussed.
Next, the strategy to use biopharmaceutical modeling in drug research and regulatory application is discussed. Introduction of good simulation practice for biopharmaceutical modeling would be an emergent issue for regulatory application.
Many figures and tables are provided to make it easy to understand biopharmaceutical modeling. In addition, more than 900 references are cited. I hope that readers will enjoy reading this book and that this book will be a helpful reference for biopharmaceutical modeling.
I would like to thank Mr. Jonathan Rose of John Wiley & Sons, Inc. for giving me this opportunity to write a book about biopharmaceutical modeling.
I would like to thank Dr. Takashi Mano and Dr. Ravi Shanker for carefully reading my manuscript and giving me valuable advice. They also supported the investigation of biopharmaceutical modeling at Pfizer. I also thank Dr. Brian Henry, Dr. Mark McAllister, and Ms. Nicola Clear for their kind support at Pfizer. The scientific discussion with the Pfizer biopharmaceutics group members improved my understanding of this subject. The suggestions from Prof. Steve Sutton, Dr. Kazuko Sagawa, and Ms. Kelly Jones about in vivo physiology are greatly appreciated. Ms. Joanne Bennett kindly lectured me about the cell culture models. I would like to thank Dr. Claudia da Costa Mathews, Dr. Hannah Pearce, Dr. Sue Mei Wong, Mr. Simon Pegg, Mr. Neil Flanagan, Mr. Mike Cram, Mr. Unai Vivanco, Ms. Sonia Patel, and Mr. Richard Manley for investigating the enabling formulations and physchem screening. I would like to thank the Pfizer Pharmaceutical Science members for supporting and inspiring me to pursue the sciences and practical drug research work. I would like to thank Dr. Tomomi Mastuura for her instructions about pharmacokinetics. I would like to thank Dr. Stefan Steyn for implementation of biopharmaceutical modeling in early drug discovery.
Thanks also goes to the Pfizer Nagoya Pharm R&D members. Mr. Shohei Sugimoto, Dr. Toshiyuki Niwa, Dr. Naofumi Hashimoto, Mr. Akinori Ito, Dr. Takashi Kojima, Mr. Omura Atsushi, and Mr. Morimichi Sato kindly taught me solid-state chemistry and enabling formulations. I would like to thank Mr Arimich Okazaki, Mr. Yohei Kawabata, Ms. Keiko Kako, Dr. Sumitra Tavornvipas, Ms. Akiko Suzuki, Ms. Tomoko Matsuda, and Ms. Shiho Torii for kindly working together toward progress of the science at the Nagoya site.
I would like to thank Dr. Ryusuke Takano of Chugai Pharm. for his excellent works on biopharmaceutical sciences. I also would like to thank the Chugai physicochemical and pharmacokinetics group members, especially Mr. Hirokazu Hamada, Dr. Noriyuki Takata, Dr. Akiko Koga, Mr. Ken Goshi, Dr. Kazuya Nakagomi, Mr. Ro Irisawa, Ms. Harumi Onoda, Dr. Hidetoshi Ushio, Dr. Yoshiki Hayashi, Dr. Yoshiaki Nabuchi, Dr. Minoru Machida, and Dr. Ryoichi Saito. They brought me up as an industrial scientist. I would like to thank Dr. Ken-ichi Sakai and Mr. Kouki Obata for working with me toward progress of the sciences at Chugai.
I would like to thank Dr. Alex Avdeef for finding a young scientist at a rural countryside in Japan and introducing him to the world. I greatly appreciate the kind support from the UK physicochemical scientist community, especially, Dr. John Comer, Dr. Karl Box, Dr. Alan Hill, Dr. Nicola Colclough, Dr. Toni Llinas, Dr. Darren Edwards, and other scientists. Their kind support made my UK life enjoyable and fruitful. I would also like to thank Prof. Amin Rostami-hochaghan, Dr. David Turner, Dr. Sibylle Neuhoff, and Dr. Jamai Masoud of SimCYP. I would like to thank Prof. Per Artursson, Dr. Manfred Kansy, Dr. Bernard Faller, Dr. Edward Kerns, and Dr. Li Di for discussions about PAMPA. I would like to thank Dr. Lennart Lindfors for constructive discussions.
I greatly appreciate the mentorship of Prof. Katsuhide Terada and Prof. Shinji Yamashita. I also would like to thank Dr. Makoto Kataoka and Dr. Yoshie Masaoka for the collaboration works.
Finally, I would like to express my greatest thanks to my wife, Hitomi. Without her dedicated support, I could not have gone through the tough task of writing a book like this. I sincerely dedicate this book to her.
Kiyo Sugano
References
1. Amidon, G.L., Lennernas, H., Shah, V.P., Crison, J.R. (1995). A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm. Res., 12, 413–420.
2. Kitano, H. (2002). Computational systems biology. Nature, 420, 206–210.
List of Abbreviations
Chapter 1
Introduction
“The eternal mystery of the world is its comprehensibility. The fact that it is comprehensible is a miracle.”
—Albert Einstein
The aim of this chapter is to discuss the whole picture of oral absorption of a drug in a comprehensive and descriptive manner without using any mathematical equation.
1.1 An Illustrative Description of Oral Drug Absorption: The Whole Story
The oral absorption of a drug is a sequential process of dissolution and intestinal membrane permeation of a drug in the gastrointestinal (GI) tract (Fig. 1.1).
Figure 1.1 Schematic presentation of oral drug absorption processes: disintegration, dissolution, permeation, and transit.
After dosing a drug product (e.g., tablet and capsule), the formulation disintegrates to release solid particles of active pharmaceutical ingredient (API) in Fig. 1.1). The released API particles then dissolve into the GI fluid as molecularly dispersed drug molecules . The maximum amount of a drug dissolved in the GI fluids is limited by the solubility of the drug in the fluids. In some cases, after an initial API form (such as a salt form) being dissolved, a transient supersaturated state is produced, and then, another solid form (i.e., a free base or an acid) can precipitate out in the intestinal fluid via nucleation . The dissolved drug molecules are conveyed close to the intestinal wall by the macromixing of the intestinal fluid and further diffuse through the unstirred water layer (UWL), which is adjacent to the epithelial cellular membrane . The drug molecules then permeate the apical membrane of the epithelial cells mainly by passive diffusion but in some cases, via a carrier protein (a transporter) such as PEP-T1 . If the drug is a substrate for an efflux transporter such as P-gp, a portion of the drug molecules is carried back to the apical side . Some drugs pass through the intercellular junction (the paracellular route) . In the epithelial cells, the drug could be metabolized by enzymes such as CYP3A4 . After passing through the basolateral membrane , the drug molecules reach the portal vein. The drug molecules in the portal vein then pass through the liver and reach the systemic circulation .
1.2 Three Regimes of Oral Drug Absorption
The central dogma of oral drug absorption is the interplay between solubility, the dissolution rate and permeability of a drug. On the basis of the central dogma, the three rate-limiting steps of oral absorption can be defined. Crystal clear understanding of these regimes is the first step toward understanding biopharmaceutical modeling [1]. Figure 1.2 shows the schematic presentation of the rate-limiting steps in the oral absorption of a drug [2].
Dissolution rate-limited absorption (DRL) (
Fig. 1.2a
)
Permeability-limited absorption (PL) (
Fig. 1.2b
)
Solubility-permeability-limited absorption (SL) (
Fig. 1.2c
)
Figure 1.2 Rate-limiting steps in oral absorption of a drug represented by the bucket model [2]. (a) Dissolution rate limited; (b) permeability limited; and (c) solubility–permeability limited.
Figure 1.3 The effect of dose and particle size in each rate-limiting step cases.
Figure 1.4 Typical dose–absorbed amount relationship.
The balance of the dissolution rate coefficient (kdiss), the permeation rate coefficient (kperm) and the ratio of dose strength to the solubilization capacity of the GI fluid (Dose/Sdissolv × VGI) determines the regime of oral drug absorption. The last parameter is called the dose number (Do). The dose number is one of the most important parameters in biopharmaceutical modeling.
1.3 Physiology of the Stomach, Small Intestine, and Colon
The GI tract can be roughly divided into the stomach, the small intestine, and the colon (Fig. 1.5). In humans, the pH of the stomach fluid is 1.2–2.5 in the fasted state but 5–6 in the fed state. The fluid volume in the stomach is ca. 30 ml. The pH of the intestinal fluid is 6.0–7.0 and is maintained relatively constant. The fluid volume in the small intestine is ca. 100–250 ml. Bile acid concentration in the small intestine is ca. 3 mM in the fasted state and 10–15 mM in the fed state. The pH of the colonic fluid is 6.0–8.0. The fluid volume in the colon is ca. 15 ml.
Figure 1.5 Gastrointestinal tract and key characteristics.
Drug absorption mainly occurs in the small intestine as it has the largest absorptive surface area and the largest fluid volume in the GI tract. Bile micelles can enhance the solubility and dissolution rate of a lipophilic drug. The stomach pH can affect the solubility and dissolution of a free base and its salt. It can also affect the precipitation of free acid from its salt. For low permeability and/or low solubility drugs, colonic absorption is usually negligible because of the small absorptive surface area, small fluid volume, solidification of the fluid, lack of bile micelles, etc.
1.4 Drug and API Form
The patterns of oral drug absorption can be also categorized from the viewpoint of the properties of the drug molecule and API solid form.
A drug can be categorized as undissociable or dissociable ones. The dissociable drug is then further categorized as acid, base, or zwitterions. The API solid form of an acid, base, and zwitterion can be categorized as a free form or a salt form (e.g., HCl salt of a base). For PL cases, the difference of a solid form does not affect the oral absorption of a drug. On the other hand, for DRL and SL cases, the solid form of a drug has a significant impact on the oral absorption of a drug.
1.4.1 Undissociable and Free Acid Drugs
In the case of undissociable drugs and free acid drugs, the effect of the stomach pH on the solubility and dissolution rate of the drug is negligible. This is the simplest cases for biopharmaceutical modeling. A practically reasonable predictability is anticipated (Chapter 8) [4].
1.4.2 Free Base Drugs
Free base drugs dissolve better in the low pH environment of the stomach than in the small intestine. However, as the stomach contents move into the small intestine, the pH is neutralized and the solubility of the drug is decreased. The drug particles, which once reduced its size by dissolution in the stomach, regrows in the small intestine (the dissolved drug molecule moves back to the solid surface of the free base particles) [5]. The biopharmaceutical modeling for this case is simpler compared to the salt cases. A practically reasonable predictability is anticipated (Chapter 8) [6].
1.4.3 Salt Form Cases
In the case of salts, the oral absorption process is much more complex. A salt form drug usually dissolves rapidly in the GI fluid. However, once the dissolved drug concentration hits the critical supersaturation concentration, the free form drug precipitates out as a solid. To represent this phenomena in biopharmaceutical modeling, a nucleation theory has to be taken into account [7]. However, little is known about the nucleation of drug molecules in the GI environment. Therefore, the extent and duration of supersaturation in the GI tract is currently not quantitatively predictable from in vitro data. A similar scenario can be applied for cocrystalline, amorphous solid form, and supersaturable formulations. To improve the biopharmaceutical modeling in the future, this area requires significant investigations.
1.5 The Concept of Mechanistic Modeling
The mechanistic modeling approach is pursued in this book. To enable computational simulation, the processes that consist of drug absorption must be reduced down to the molecular level mechanisms. The network of theoretical equations connects the overall processes of drug absorption from the molecular level mechanism to the plasma concentration (Cp) time profile of a drug in humans (can be further connected to pharmacological effects via pharmacokinetic–pharmacodynamic (PKPD) modeling). The whole network of theoretical equations of oral drug absorption is called the gastrointestinal unified theoretical (GUT) framework in this book. As described above, oral drug absorption consists of four main processes, dissolution, permeation, nucleation, and GI transit. These processes are further reduced down to the molecular level mechanisms. Ideally, all processes of oral drug absorption should be described by mechanistic mathematical equations that have physical meanings at the molecular level. Therefore, the GUT framework shares the same philosophy of the “analysis”–“synthesis” approach employed by systems biology and physiologically based pharmacokinetic (PBPK) modeling.
Empirical multivariant statistical models (e.g., artificial neural network) are one of the other modeling approaches. Multiple drug parameters are used as input parameters and connected to outcome values using linear or nonlinear empirical equations. There are many investigations applying this approach for the prediction of oral drug absorption 8–11. However, in the GUT framework, this approach is not pursued unless otherwise inevitable.
The “analysis” (reductionist) approach is rather a traditional approach in the history of science since Galileo's era, and this approach has been incredibly successful. This approach revealed many mysteries of astronomy, physics, chemistry, and finally, biology. However, an analytical understanding of each part does not mean the understanding of relationship between each part and their role on the total performance. For example, understanding of enzyme-level activity is not sufficient (but necessary) to understand how our brain works. With the aid of computational power, the “synthesis” approach has become available. We can now understand the relationship between primary processes and simulate the total performance. In computational systems biology, the networks of enzyme reactions and their effect on the phenotype is described by mechanistic mathematical models. By pursuing this approach, we will be able to model the disease state and find a clue to cure the patient. In this book, we pursue the same approach with systems biology. However, in addition to biological processes, the drug substance and formulation perspectives must be taken into account in biopharmaceutical modeling. By using the mechanistic modeling approach, we will be able to control the total bioperformance of a drug by designing the molecular structure, API form, and formulation of the drug.
The mechanistic biopharmaceutical modeling consists of theoretical model equations, physiological parameters, and drug parameters (Fig. 1.6). All of these factors significantly affect the performance of biopharmaceutical modeling. The physiological and drug parameters are often thought to have less error than the mechanistic model equations. However, this notion is incorrect. The physiological data in the literature have large variation and some physiological parameters have not been obtained yet. In addition, a drug parameter can have a large error (variation) when an experiment is not properly performed. Even in the case of solubility measurement, it often has more than twofold variation for low solubility drugs. Therefore, in addition to the theoretical models, the physiological and drug parameters are also discussed in Chapters 6 and 7.
Figure 1.6 Physiological and drug parameters and theoretical equations.
The first step to construct the GUT framework is the unification of the concept of dissolved drug concentration (Cdissolv) [7]. We will start the next chapter with defining the dissolved drug concentration.
Notes
1 In some cases, this dose subproportionality in oral absorption can be cancelled out by supraproportionality in systemic elimination clearance (Section 5.5.3).
References
1. Yu, L.X. (1999). An integrated model for determining causes of poor oral drug absorption. Pharm. Res., 16, 1883–1887.
2. Sugano, K., Okazaki, A., Sugimoto, S., Tavornvipas, S., Omura, A., Mano, T. (2007). Solubility and dissolution profile assessment in drug discovery. Drug Metab. Pharmacokinet., 22, 225–254.
3. Sugano, K., Kataoka, M., Mathews, C.d.C., Yamashita, S. (2010). Prediction of food effect by bile micelles on oral drug absorption considering free fraction in intestinal fluid. Eur. J. Pharm. Sci., 40, 118–124.
4. Sugano, K. (2011). Fraction of a dose absorbed estimation for structurally diverse low solubility compounds. Int. J. Pharm., 405, 79–89.
5. Johnson, K.C. (2003). Dissolution and absorption modeling: model expansion to simulate the effects of precipitation, water absorption, longitudinally changing intestinal permeability, and controlled release on drug absorption. Drug Dev. Ind. Pharm., 29, 833–842.
6. Sugano, K. Computational oral absorption simulation of free base drugs. (2010). Int. J. Pharm., 398(1–2), 73–82.
7. Sugano, K. (2009). Introduction to computational oral absorption simulation. Expert Opin. Drug Metab. Toxicol., 5, 259–293.
8. Wessel, M.D., Jurs, P.C., Tolan, J.W., Muskal, S.M. (1998). Prediction of human intestinal absorption of drug compounds from molecular structure. J. Chem. Inf. Comput. Sci., 38, 726–735.
9. Turner, J.V., Maddalena, D.J., Agatonovic-Kustrin, S. (2004). Bioavailability prediction based on molecular structure for a diverse series of drugs. Pharm. Res., 21, 68–82.
10. Klopman, G., Stefan, L.R., Saiakhov, R.D. (2002). ADME evaluation. 2. A computer model for the prediction of intestinal absorption in humans. Eur. J. Pharm. Sci., 17, 253–263.
11. Tian, S., Li, Y., Wang, J., Zhang, J., Hou, T. (2011). ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints. Mol. Pharm., 8, 841–851.
Chapter 2
Theoretical Framework I: Solubility
“Everything should be made as simple as possible, but not simpler.”
—Albert Einstein
Figure 2.1 shows the network of equations, which consist of the gastrointestinal unified theoretical framework (GUT framework) [1]. The GUT framework is discussed in the following four sections. This framework is constructed based on the unified definition of “dissolved drug concentration (Cdissolv)” and “fraction (f)” of each molecular species.
Figure 2.1 Network of equations consisting of the GUT framework.
2.1 Definition of Concentration
Even though the definitions of concentration look trivial and often being omitted in the literature, clear understanding of this point is important for biopharmaceutical modeling.1
2.1.1 Total Concentration
Total concentration of a drug (Ctot) is the amount of a drug substance in a fluid, regardless of the substance being undissolved solid or dissolved molecules. For example, when 100 mg of a solid drug is diluted to 1 ml with a fluid, the concentration is 100 mg/ml, regardless of whether the drug is completely dissolved or not in the fluid. This point is often miscommunicated by a formulation scientist and a biologist. Biologists often tacitly assume complete dissolution in the assay media, whereas a formulation scientist uses this expression for a suspension formulation.
2.1.2 Dissolved Drug Concentration
“Dissolved drug concentration (Cdissolv)” is used in this book to express the concentration of dissolved drug molecules in the fluid. Drug molecules can exist in various states in a fluid (Fig. 2.2). After adding a solid compound to a blank medium, if it looks transparent to the eye, we often say it is “dissolved” and the medium is typically called a solution. However, the molecule can exist in this transparent solution as (i) a monomer (a single molecule surrounded by solvent molecules), (ii) a dimer or higher self-aggregate, (iii) complexes with large molecules (such as cyclodextrin), (iv) the micelle included state, or even (v) nanoscale particles. In the literature, with the exception of the last case, these are referred to as solubilized (the last example is often referred to as nanosuspension). We use this definition of “solution” in this chapter unless otherwise noted. In this book, undissociated monomer molecules, dissociated monomer molecules, and bile-micelle-bound molecules are considered in the theoretical framework unless otherwise noted. The dissolved drug concentration (Cdissolv) in the gastrointestinal (GI) fluid is expressed as the sum of each species as
(2.1)
(2.2)
where X is the amount of drug (weight or mole) and C is the concentration (X/VGI). The subscripts u, z (expressed as + , − , + + , − − , … in the following sections), and bm indicate unbound monomer molecules, charge of molecules, and bile-micelle-bound monomer molecules, respectively. VGI is the fluid volume in a GI position.
Figure 2.2 Dissolved drug molecules in the gastrointestinal fluid.
2.1.3 Effective Concentration
The effective concentration for a reaction, such as dissolution and permeation, depends on the “availability” of the molecular state for the reaction. For example, the dissolution of drug particles can be carried out as both the unbound monomer and the bile-micelle-included state. On the other hand, passive transcellular permeation across the intestinal epithelial membrane occurs mainly for unionized unbound monomer molecules (pH partition and free fraction theories) (Fig. 2.3).
Figure 2.3 Schematic representation of dissolution and permeation.
The effective concentration of a reaction is expressed as the fraction of the dissolved drug concentration. For example, concentration of the undissociated unbound monomer molecule is expressed as
(2.3)
where fu is the fraction of unbound monomer molecules and f0 is the fraction of undissociated molecules. This expression is the same as that for plasma concentration and unbound fraction used in pharmacokinetics (PK).
2.2 Acid–Base and Bile-Micelle-Binding Equilibriums
The fraction of undissociated monomer molecule (f0) is determined by the dissociation constant (pKa) of a drug and pH of the fluid. The famous Henderson–Hasselbalch (HH) equation is derived from the acid–base chemical equilibrium equation.2 The derivation of the HH equation is often omitted in a standard textbook of pharmacy; however, it is very important for the clear understanding of biopharmaceutical modeling (Table 2.1).
Table 2.1 Fraction of Undissociated Species
2.2.1 Monoprotic Acid and Base
In the case of an acid, the chemical equilibrium can be written as Equation 2.4 (cf. the parenthesis “[]” indicates the “dissolved drug concentration” of the molecular species (Section 2.1)).
(2.4)
(2.5)
(2.6)
In the case of a base,
(2.7)
(2.8)
Therefore,
(2.9)
Note that the position of the undissociated and the charged drug3 concentrations in the equation is swapped as for an acid case. When the pH is higher (the fluid is alkaline, the proton concentration is lower), the fraction of undissociated molecules increases ([H+]/Ka becomes smaller in Equation 2.9).
At pH 2,
Similarly, at pH 4,
And at pH 6,
2.2.2 Multivalent Cases
For a divalent acid,
(2.10)
(2.11)
The fraction of the undissociated molecular species is then given as
(2.12)
An equation for a divalent base can be derived similarly.
Zwitter ionic cases are much more complex, as both of undissociated and zwitter ionic species are of charge neutral (Fig. 2.5) [2]. To calculate the fractions of undissociated and zwitter ionic species (f0 and f+−, respectively), the microscopic pKa value have to be obtained. However, there is no simple experimental method to determine the microscopic pKa (Section 7.2).
Figure 2.5 (a) Micro pKa and (b) macro pKa.
2.2.3 Bile-Micelle Partitioning
The bile-micelle partitioning is another important equilibrium of drug molecules in the intestinal fluid. Drug molecules bound to bile micelles behave differently from unbound ones during dissolution and permeation of the drug. Therefore, the bile-micelle-unbound fraction (fu) has to be explicitly taken into account for biopharmaceutical modeling. The bile-micelle binding can be treated in a similar way to acid–base equilibrium.4 Since it is difficult to define the concentration of micelles, the bile-micelle partition coefficient (Kbm) is usually defined based on the bile acid concentration ([M]) [3].
(2.13)
(2.14)
The bile-micelle partition coefficient changes depending on the molecular charge, that is, Kbm, 0 for the undissociated molecule, Kbm, − the for monoprotic anion, and Kbm, + for monoprotic cation are different. The Kbm values can be back-calculated from the solubility values in a bile-micelle media (such as FaSSIF, Section 7.6.2) and its blank media.
2.2.4 Modified Henderson–Hasselbalch Equation
Finally, when all the equilibriums are taken into account 4–6, the fraction of the unbound undissociated monomer molecule (Cu, 0/Cdissolv) for acid is
(2.15)
Similarly, for a monoprotic base,
(2.16)
These equations are called modified HH equation in this book. The pH solubility profile of dipyridamole in a biorelevant media containing bile micelles is shown in Figure 2.6 [1, 3].
Figure 2.6 pH solubility profile of dipyridamol in biorelevant media containing bile micelles [3].
2.2.5 Kbm from Log Poct
Kbm can be roughly calculated from the octanol water partition coefficient (Poct) as [3]
(2.17)
The bile-micelle partition coefficients of monocation and anion (Kbm, + and Kbm, −, respectively) can be estimated as [7, 8]
(2.18)
(2.19)
2.3 Equilibrium Solubility
2.3.1 Definition of Equilibrium Solubility
The solubility of a drug is defined based on the equilibrium state between the dissolved drug molecules and the undissolved solid drug molecules (Figs. 2.7 and 2.2).5 At equilibrium, the chemical potential at the solid surface (free energy/mole) is equal to that in the fluid. When we look at the solid surface at a molecular level, there is a dynamic equilibrium determined by the balance of detaching and attaching rates (Fig. 2.8). The term thermodynamic solubility is also used in the literature but not used in this book.
Figure 2.7 (a) Complete and (b) incomplete dissolution of a drug in a fluid.
Figure 2.8 Detachment of a molecule from the solid surface and concentration gradient in the diffusion layer.
To measure the solubility of a drug, the amount of the drug (Dose) added to the fluid must exceed the solubilization capacity of the fluid, that is, solubility × fluid volume.6 The dose number (Do) is defined as (in a broad sense)7
(2.20)
For the Do > 1 cases, when a solid drug is added to the fluid, a portion of the added drug remains undissolved in the fluid. For example, when 10 mg of a drug with an equilibrium solubility of 1 mg/ml is added to 2 ml of the fluid, Do is 5(= 10 mg/(1 mg/ml × 2 ml). In this case, 2 mg gets dissolved and 8 mg remains undissolved. When Do < 1, the drug completely dissolves in the fluid (Fig. 2.7). For example, when the above drug is added to 20 ml of the fluid, Do is 0.5 (= 10 mg/(1 mg/ml × 20 ml).
The concept of the dose number can be expanded and generally defined when a solid material is added to a fluid. The dose number determines whether a portion of the solid drug remains undissolved in the fluid and participates in the equilibrium network of drug molecules in the fluid (Fig. 2.7). In the absence of the undissolved drug in the fluid (i.e., Do < 1, the drug is completely dissolved in the fluid), the equilibriums in the solution are sufficient to describe the concentration of each molecular species in the fluid, for example, pH equilibrium and bile-micelle-binding equilibrium. However, in the presence of undissolved drug material (i.e., Do > 1), the equilibrium between the solid drug (remaining undissolved) and the dissolved drug have to be additionally taken into account.
2.3.2 pH–Solubility Profile (pH-Controlled Region)
The typical pH–equilibrium solubility profile of a monobasic compound is shown in Figure 2.9. The pH–solubility profile can be divided into “pH” and “common ionic effect” controlled regions. The pH–solubility profile of a drug in a simple buffer (without solubilizers such as micelles) is controlled by the pKa, intrinsic solubility (S0), and solubility product (Ksp) of a drug, as well as the pH and the common ion concentration in the fluid. The smaller value of the pH-controlled or common-ion-controlled solubility determines the actual solubility of a drug experimentally observed.
Figure 2.9 Typical pH–solubility profile of a base.
The pH–solubility curve in the pH-controlled region is derived as follows. In the case of an acid, when an excess amount of a solid drug coexists in a fluid at a pH where no dissociation occurs (i.e., pH pKa of the drug), the equilibrium between the solid and the dissolved drug is written as
(2.21)
where 〈AH〉 represents the solid form of the undissociated drug (cf. [] indicates the “dissolved drug concentration” (Section 2.1). When the system is at equilibrium in this pH region, [AH] equals the intrinsic solubility of the undissociated drug (S0).
As the pH goes up, the acid molecules start to dissociate. Therefore, we add a pH–pKa equilibrium:
(2.22)
(2.23)
As far as the solid form of the undissociated acid coexists in equilibrium with [AH] (i.e., Do > 1), the concentration of the dissolved free acid ([AH]) remains constant and equals to S0. As described in Section 2.2.1, [A−] can be determined by pKa, pH, and [AH].
(2.24)
(2.25)
When we rewrite this,
(2.26)
In Figure 2.10, the concept of concentration, fraction, and solubility are illustrated.
Complete Dissolution Case (Do < 1)
Incomplete Dissolution Case (Do > 1)
2.3.3 Solubility in a Biorelevant Media with Bile Micelles (pH-Controlled Region)
As discussed in Section 2.2.4, the bile-micelle-binding equilibrium can be treated in a similar way to acid–base equilibrium. The solubility of undissociable acid and base drugs in a biorelevant media with bile micelles (Sdissolv) can be written as
(2.27)
(2.28)
(2.29)
2.3.4 Estimation of Unbound Fraction from the Solubilities with and without Bile Micelles
From Equation 2.29, the unbound fraction (fu) can be back-estimated from the solubilities in the media with and without bile micelles (Sdissolv and Sbuffer, respectively).
(2.30)
This method is practically useful as Sblank and Sdissolv is usually available during drug discovery.
It should be emphasized that, as in the same manner with acid–base equilibrium, when the fluid is in equilibrium with excess amount of a solid drug (i.e., Do > 1), even when bile-micelle concentration is increased, the concentration of unbound drugs remains constant (and equals Sblank), whereas Cdissolv(=Sdissolv) is increased and the fractions of unbound drugs is decreased. On the other hand, when the drug is completely dissolved in the fluid (i.e., Do < 1), both the concentration and the fraction of unbound drugs are decreased as bile-micelle concentration is increased. This point is important especially when considering the food effects on oral absorption of a drug, as the food intake increases the bile-micelle concentration in the GI tract (Sections 12.2.2.1 and 12.2.3.1).
2.3.5 Common Ionic Effect
The solubility of a salt is described by the solubility product (Ksp). In the case of a salt of base drug,
(2.31)
(2.32)
where the subscript “sat” indicates the saturated species (species of equilibrium maker), and 〈BH+X− 〉 is the activity of the solid part of the salt and is defined as 1. Therefore,
(2.33)
When we consider the case that the fluid pH is adjusted by an acid, HX (e.g., HCl) and ionic strength is adjusted by a salt, MX (e.g., NaCl), because of the charge neutrality in the fluid, the sum of the anions (= [X−] + [OH−]) equals the sum of the cations (= [H+] + [BH+] + [M+]).
(2.34)
By inserting this charge neutrality equation into the solubility product equation,
(2.35)
(2.36)
The dissolved drug solubility is the sum of B and BH+. Therefore, using the HH equation for mono bases,
(2.37)
where pHmax is the pH of the maximum solubility, the system changes from the pH-controlled region to the common-ion-effect-controlled region. Similar equation can be derived for acid drugs.
In the pH-controlled region (acid: pH < pHmax, base: pH > pHmax), the slope of the logarithmic pH–solubility plot is 1. Therefore, one unit shift of pH or pKa results in 10-fold change in solubility. The maximum solubility of the pH–equilibrium solubility profile is limited by the solubility product. In the common-ion-effect-controlled region, the equilibrium solubility of a drug depends largely on the concentration of the counterions (common ion effect) but less on the pH (concentration of H3O+). Therefore, the species of the counterion is an important factor when we measure the pH–equilibrium solubility profile (Ksp is different among the counterion species such as Cl−, CH3SO3−).
Na+ and Cl− are most often used, as they are the major ionic species in the physiological condition.
In this book, the intrinsic solubility of a salt (Ssalt) is defined as
(2.38)
2.3.6 Important Conclusion from the pH–Equilibrium Solubility Profile Theory
From the theories of the pH–equilibrium solubility profile, it is concluded that, regardless of the initial solid form (free or salt) used for a solubility measurement, the pH–equilibrium solubility profile becomes identical in the pH-controlled region.9Figure 2.11 shows some experimental data [10]. For example, even when we start with an HCl salt of a base, as the pH is titrated above the pHmax, the free base precipitates out and Sdissolv is determined based on the equilibrium with the solid of the free base (not HCl salt). In other words, the pH–equilibrium solubility profiles measured from a free base and its salt become identical when the pH is well maintained by the buffer. This situation is very different from the equilibrium solubility in an unbuffered media (i.e., pure water), as the initial pH can be shifted by the added drug. In this case, the final pH and the equilibrium solubility become different depending on the starting solid material. In drug discovery, a strong buffer (e.g., 50 mM phosphate buffer) is often used for the solubility measurement. Therefore, an identical (or very similar) solubility value is usually reported for a free drug and its salt.
Figure 2.11 pH solubility profile of (a) salicylic acid and (b) theophylline measured from the free acids and the sodium salts. (a) The pH–solubility profiles of salicylic acid (free acid, circle) and sodium salicylate (triangle). Points A (pH 2.3) and B (pH 6.9) represent the pH values and concentrations of saturated solutions of salicylic acid and its sodium salt in pure water. (b) The pH solubility profiles of theophylline (free acid, circle) and sodium theophylline (triangle). Source: Adapted from Reference 10 with permission.
Even though the equilibrium solubility measured from a free form and a salt form becomes the same in a buffer, the bioavailabilities of a free base and its salt are usually significantly different. This suggests that the equilibrium solubility in a buffer at a pH cannot be simply used for biopharmaceutical modeling of a salt (as it is identical to a free base).10 The reasons that salt formation increases the oral absorption of a poor solubility drug are (i) salt formation increases the dissolution rate (by increasing the solid surface solubility), and/or (ii) a supersaturated concentration can be produced in the gastrointestinal fluid after the dissolution of a salt (Sections 3.3 and 11.1) (the dissolved drug molecules at the transient supersaturated concentration are absorbed before the dissolved drug concentration settle down to the equilibrium solubility (which is identical to that of the free base form)). The difference in the dissolution and precipitation mechanisms between a free form and a salt should be taken into account in biopharmaceutic modeling.11,12
2.3.7 Yalkowsky's General Solubility Equation
The intrinsic solubility of a drug (free form) in water is determined by the hydration energy of a drug molecule and the sublime energy (Fig. 2.12). Yalkowsky's general solubility equation is a simple but very useful equation [11, 12].
(2.39)
(2.40)
where ΔSf is the entropy of fusion, n is the number of nonhydrogen atoms (n > 5) in a flexible chain, and m.p. is the melting point of a drug. This equation can be further simplified to
(2.41)
Figure 2.12 Sublime and hydration.
In this equation, the log Poct reflects the hydration energy, and the melting point reflects the crystal lattice energy. Roughly speaking, a change in m.p. of 100°C will change the solubility 10-fold. This equation cannot be applied for enantiotropic polymorph cases (Section 7.5.2.4). The average error of this equation is 0.42 log units [13].
Equations 2.40 and 2.41
