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Computational methods for the prediction of ADME and Toxicity
Advanced Drug Delivery Reviews 54 (2002) 253–254 www.elsevier.com / locate / drugdeliv
Preface
Computational methods for the prediction of ADME and Toxicity As it enters the 21 st century, the pharmaceutical industry faces many challenges. Not the least of these is the need to reduce the number of compounds entering the (pre)clinical phases of drug development that fail at subsequent hurdles on the way to market. The recognition that many of these failures (estimates range upwards from 40%) are due to the poor biopharmaceutical properties of the candidate compound has resulted in a major drive to identify and remove compounds with poor ADMET (Absorption, Distribution, Metabolism, Elimination, Toxicity) profiles as early as possible in the drug discovery process. Thus, a paradigm shift has occurred in the initial phases of drug discovery: nowadays, in addition to paying attention to the traditional concern of attaining potency and selectivity towards the biological target of interest, companies also take account of ADMET considerations at an early stage, embracing a ‘‘fail fast, fail cheap’’ philosophy. Advances in automation technology and experimental ADMET techniques, both in vitro and in vivo, have enabled the assaying of much larger numbers of compounds than was traditionally possible. Consequently, compounds can be assessed for ADMET properties much earlier in their lifetime, often in parallel with assays for potency and selectivity, making lead optimisation a truly multi-parametric activity. In addition to the development of experimental assays with greater throughput, there has been considerable effort applied to the conception and validation of computational methods for predicting ADMET-related properties. Compared to experimental approaches, these in silico methods have the advantage that they do not require compound synthesis. They can therefore be
applied to ‘‘virtual’’ compounds permitting the rapid exclusion of likely failures at the ‘‘drawing-board’’ stage. It is the purpose of this special theme issue of Advanced Drug Delivery Reviews to survey the current status of the various computational methods for ADMET prediction. The issue opens with a review by Pat Walters and Mark Murcko (Vertex Pharmaceuticals Inc., Cambridge, MA) of methods for the prediction of ‘‘drug-likeness’’ in a general sense. Such methods are especially useful for rapid assessment of large compound collections or virtual libraries. The preferred route of administration for the majority of therapeutics is by oral ingestion. Thus, a key ADMET quantity is intestinal permeability, a prerequisite for oral bioavailability. The progress in computational methods for the prediction of this quantity is discussed in Chapter 2 by Bill Egan (Vertex Pharmaceuticals Inc., Cambridge, MA) and Georgio Lauri (Accelrys, Inc., Princeton, NJ). Apart from the intestinal mucosa, another key physiological barrier is that between the systemic circulation and the brain – the so-called blood-brain barrier (BBB). In some circumstances, it is essential that candidate compounds be able to permeate the BBB, for instance if the biological target exists in the brain; in other cases, brain penetration might need to be minimised to reduce the opportunity for sideeffects. In Chapter 3, the current state-of-the-art in predicting BBB penetration is surveyed by Ulf Norinder and Markus Haeberlein (AstraZeneca, ¨ ¨ Sodertalje, Sweden). A confounding factor for the drug designer at both the intestinal mucosa and the blood-brain barrier is the presence of the P-glycoprotein efflux pump,
0169-409X / 02 / $ – see front matter 2002 Elsevier Science B.V. All rights reserved. PII: S0169-409X( 02 )00002-9
254
Preface
which can act to hinder the penetration of drug compounds. Clearly, a computer-based method able to predict substrates of this transporter would be very useful. Progress towards this end is reviewed in Chapter 4 by Terry Stouch (Bristol-Myers Squibb, Princeton, NJ) and Olafar Gudmundsson (Genentech, Inc., San Francisco, CA). On the other hand, there exist transport systems that actively promote absorption of some substances across physiological barriers. Here again, the ability to predict whether or not a compound may be able to take advantage of one of these active transporters would be of use. Chapter 5 by Eric Zhang, Mitch Phelps, Chang Cheng, and Peter Swaan (Ohio State University, Columbus, OH) together with Sean Ekins (Eli Lilly and Co., Indianapolis, IN) provides a comprehensive discussion of emerging in silico methods in this area. It is being recognised increasingly that aqueous solubility is a key factor for oral bioavailability and accurate prediction of solubility from structure remains an outstanding challenge for computational chemists. Much effort has been expended in this direction in recent years – this is summarised in Chapter 6 by Bill Jorgensen (Yale University, New Haven, CT) and Erin Duffy (Achillion Pharmaceuticals, Inc., New Haven, CT). Turning from absorption prediction, the next three chapters review progress in various computational approaches to the prediction of metabolism. In Chapter 7, Marcel de Groot (Pfizer Ltd., Sandwich, UK) and Sean Ekins (Eli Lilly and Co., Indianapolis, IN) describe pharmacophore-based methods for the prediction of metabolism by cytochrome P450 (Cyp450) enzymes. Following on from this, in Chapter 8, Stewart Kirton, Carol Baxter and Michael Sutcliffe (University of Leicester, Leicester, UK) provide a tutorial and review on the subject of the
comparative modelling of the same enzymes. This is a key procedure, given that at the present time, no experimental structures exist for the human enzymes. Finally in this section, Tony Long and Jan Langowski (LHASA Ltd., Leeds, UK) survey the current commercially available computer systems for the prediction of metabolism. The final piece of the ADMET jigsaw is toxicology. Computer-based systems for predicting whether or not a compound is likely to show a toxic effect have been in development for at least a decade. The state-of-the-art in this field is reviewed by Nigel Greene (Pfizer Inc., Groton, CT). The closing chapter by George Grass (Lion Biosciences, Inc., San Diego, CA) and Patrick Sinko (Rutgers University, Piscataway, NJ) looks at the developing use of computers for more detailed simulations of pharmacokinetic processes – ‘‘physiologically-based pharmacokinetic modelling’’. Taken together, these chapters provide a timely overview of a rapidly developing field. It has been my pleasure and privilege to work with this team of expert authors and I would like to express my gratitude to them for their contributions to this theme issue. I would also like to thank Dr. Philip Smith for inviting me to undertake this editorial task and for providing support and encouragement along the way. David E. Clark (Theme Editor) Argenta Discovery Ltd. 8 /9 Spire Green Centre Flex Meadow, Harlow Essex CM19 5 TR UK E-mail: [email protected]
Preface
Computational methods for the prediction of ADME and Toxicity As it enters the 21 st century, the pharmaceutical industry faces many challenges. Not the least of these is the need to reduce the number of compounds entering the (pre)clinical phases of drug development that fail at subsequent hurdles on the way to market. The recognition that many of these failures (estimates range upwards from 40%) are due to the poor biopharmaceutical properties of the candidate compound has resulted in a major drive to identify and remove compounds with poor ADMET (Absorption, Distribution, Metabolism, Elimination, Toxicity) profiles as early as possible in the drug discovery process. Thus, a paradigm shift has occurred in the initial phases of drug discovery: nowadays, in addition to paying attention to the traditional concern of attaining potency and selectivity towards the biological target of interest, companies also take account of ADMET considerations at an early stage, embracing a ‘‘fail fast, fail cheap’’ philosophy. Advances in automation technology and experimental ADMET techniques, both in vitro and in vivo, have enabled the assaying of much larger numbers of compounds than was traditionally possible. Consequently, compounds can be assessed for ADMET properties much earlier in their lifetime, often in parallel with assays for potency and selectivity, making lead optimisation a truly multi-parametric activity. In addition to the development of experimental assays with greater throughput, there has been considerable effort applied to the conception and validation of computational methods for predicting ADMET-related properties. Compared to experimental approaches, these in silico methods have the advantage that they do not require compound synthesis. They can therefore be
applied to ‘‘virtual’’ compounds permitting the rapid exclusion of likely failures at the ‘‘drawing-board’’ stage. It is the purpose of this special theme issue of Advanced Drug Delivery Reviews to survey the current status of the various computational methods for ADMET prediction. The issue opens with a review by Pat Walters and Mark Murcko (Vertex Pharmaceuticals Inc., Cambridge, MA) of methods for the prediction of ‘‘drug-likeness’’ in a general sense. Such methods are especially useful for rapid assessment of large compound collections or virtual libraries. The preferred route of administration for the majority of therapeutics is by oral ingestion. Thus, a key ADMET quantity is intestinal permeability, a prerequisite for oral bioavailability. The progress in computational methods for the prediction of this quantity is discussed in Chapter 2 by Bill Egan (Vertex Pharmaceuticals Inc., Cambridge, MA) and Georgio Lauri (Accelrys, Inc., Princeton, NJ). Apart from the intestinal mucosa, another key physiological barrier is that between the systemic circulation and the brain – the so-called blood-brain barrier (BBB). In some circumstances, it is essential that candidate compounds be able to permeate the BBB, for instance if the biological target exists in the brain; in other cases, brain penetration might need to be minimised to reduce the opportunity for sideeffects. In Chapter 3, the current state-of-the-art in predicting BBB penetration is surveyed by Ulf Norinder and Markus Haeberlein (AstraZeneca, ¨ ¨ Sodertalje, Sweden). A confounding factor for the drug designer at both the intestinal mucosa and the blood-brain barrier is the presence of the P-glycoprotein efflux pump,
0169-409X / 02 / $ – see front matter 2002 Elsevier Science B.V. All rights reserved. PII: S0169-409X( 02 )00002-9
254
Preface
which can act to hinder the penetration of drug compounds. Clearly, a computer-based method able to predict substrates of this transporter would be very useful. Progress towards this end is reviewed in Chapter 4 by Terry Stouch (Bristol-Myers Squibb, Princeton, NJ) and Olafar Gudmundsson (Genentech, Inc., San Francisco, CA). On the other hand, there exist transport systems that actively promote absorption of some substances across physiological barriers. Here again, the ability to predict whether or not a compound may be able to take advantage of one of these active transporters would be of use. Chapter 5 by Eric Zhang, Mitch Phelps, Chang Cheng, and Peter Swaan (Ohio State University, Columbus, OH) together with Sean Ekins (Eli Lilly and Co., Indianapolis, IN) provides a comprehensive discussion of emerging in silico methods in this area. It is being recognised increasingly that aqueous solubility is a key factor for oral bioavailability and accurate prediction of solubility from structure remains an outstanding challenge for computational chemists. Much effort has been expended in this direction in recent years – this is summarised in Chapter 6 by Bill Jorgensen (Yale University, New Haven, CT) and Erin Duffy (Achillion Pharmaceuticals, Inc., New Haven, CT). Turning from absorption prediction, the next three chapters review progress in various computational approaches to the prediction of metabolism. In Chapter 7, Marcel de Groot (Pfizer Ltd., Sandwich, UK) and Sean Ekins (Eli Lilly and Co., Indianapolis, IN) describe pharmacophore-based methods for the prediction of metabolism by cytochrome P450 (Cyp450) enzymes. Following on from this, in Chapter 8, Stewart Kirton, Carol Baxter and Michael Sutcliffe (University of Leicester, Leicester, UK) provide a tutorial and review on the subject of the
comparative modelling of the same enzymes. This is a key procedure, given that at the present time, no experimental structures exist for the human enzymes. Finally in this section, Tony Long and Jan Langowski (LHASA Ltd., Leeds, UK) survey the current commercially available computer systems for the prediction of metabolism. The final piece of the ADMET jigsaw is toxicology. Computer-based systems for predicting whether or not a compound is likely to show a toxic effect have been in development for at least a decade. The state-of-the-art in this field is reviewed by Nigel Greene (Pfizer Inc., Groton, CT). The closing chapter by George Grass (Lion Biosciences, Inc., San Diego, CA) and Patrick Sinko (Rutgers University, Piscataway, NJ) looks at the developing use of computers for more detailed simulations of pharmacokinetic processes – ‘‘physiologically-based pharmacokinetic modelling’’. Taken together, these chapters provide a timely overview of a rapidly developing field. It has been my pleasure and privilege to work with this team of expert authors and I would like to express my gratitude to them for their contributions to this theme issue. I would also like to thank Dr. Philip Smith for inviting me to undertake this editorial task and for providing support and encouragement along the way. David E. Clark (Theme Editor) Argenta Discovery Ltd. 8 /9 Spire Green Centre Flex Meadow, Harlow Essex CM19 5 TR UK E-mail: [email protected]