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Can the Internet help to meet the challenges inADME and e-ADME?H. van de Waterbeemd a & M. de Groot aa Department of Drug Metabolism , Pfizer Global Research and Development, PDM ,Sandwich, Kent, CT13 9NJ, UKPublished online: 29 Oct 2010.

To cite this article: H. van de Waterbeemd & M. de Groot (2002) Can the Internet help to meet the challenges in ADMEand e-ADME?, SAR and QSAR in Environmental Research, 13:3-4, 391-401, DOI: 10.1080/10629360290014269

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CAN THE INTERNET HELP TO MEET THECHALLENGES IN ADME AND e-ADME?*

H. VAN DE WATERBEEMD** and M. DE GROOT

Pfizer Global Research and Development, PDM, Department of Drug Metabolism, Sandwich, KentCT13 9NJ, UK

(Received 31 July 2001; In final form 6 September 2001)

The high-throughput screening (HTS) of large proprietary compound collections and combinatorial libraries has putan increased pressure on getting pharmacokinetic and drug metabolism data as early as possible. Properties related toabsorption, distribution, metabolism and excretion (ADME) can be estimated by a range of in vivo and in vitromethods. Most are now available or under development in high(er) throughput modus. In addition progress has beenmade in in silico methods using various QSAR and modeling techniques using a range of recently introduceddescriptors tailored to e-ADME. These approaches are promising as a filter for virtual libraries to decide on synthesisas well as in the selection of compounds for acquisition and screening.

This paper will discuss a number of Internet resources relevant to ADME studies and predictions. We havefocused on areas related to metabolism including metabolic pathways and P450 metabolism, transporters,bioavailability and absorption, pharmacokinetics and pharmacodynamics, molecular properties and tools for dataanalysis.

Keywords: ADME; Cytochrome P450; Metabolism; Molecular properties; P-glycoprotein; Pharmacokinetics

THE CHALLENGES IN ADME STUDIES

Introduction

The world of drug metabolism in an industrial setting has changed considerably over the last

5–10 years [1,2]. Once considered as a killer of compounds already taken into development,

today the drug metabolism discipline is at the center of a drug discovery project and a key

partner in a drug discovery team. Indeed in an overview of the reasons for failure it appeared

that 39% of the attrition was related to pharmacokinetics [3]. Due to the changes in chemistry

and biology towards more automated and high-throughput procedures, the challenges on

drug metabolism and pharmacokinetics studies also moved in that direction [4–6].

ISSN 1062-936X print/ISSN 1029-046X online q 2002 Taylor & Francis Ltd

DOI: 10.1080/10629360290014269

*Presented at CMTPI 2001, Computational Methods in Toxicology and Pharmacology Integratng InternetResources, July 11–13, 2001, Bordeaux, France.

**Corresponding author. Tel.: þ44-1304-646179. Fax: þ44-1304-656433. E-mail:han_waterbeemd@sandwich.pfizer.com.

SAR and QSAR in Environmental Research, 2002 Vol. 13 (3-4), pp. 391–401

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TABLE I Overview of approaches to ADME [modified after 7]

ADME property Experimental In silico

Formulation Caco-2 Prediction of polymorphismSolubility Turbidometric nephelometric, pH-metric QSAR and neural networksPermeability Lipophilicity (log P/log D ), liposomes, IAM, artificial membranes, biosensors, filter-IAM CLOGPAbsorption Caco-2, MDCK QSAR models, simulationsBioavailability Animal PK QSAR modelsVolume of distribution Animal PK log D modelMetabolism HT assays Databases, protein models, pharmacophore models, expert systemsClearance Animal PKDose prediction Allometric PBPK modeling

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High Throughput Approaches in ADME

In Table I, an overview is presented of a number of key experimental and in silico

approaches to ADME [7]. Throughput tends to increase going from in vivo, to in vitro, to

in silico, to in cerebro (Fig. 1). Much effort has been put in automating various in vitro

assays thereby helped by considerable progress in analytics, such as performant

LC/MS/MS systems. Thus now screening is in place for oral absorption potential,

including Caco-2 monolayers and various permeability models. Other screens relate to

metabolism, including the rate of metabolism using microsomes and hepatocytes,

involvement of various enzymes using individual cDNAs, and studies for the potential for

drug–drug interactions. Increasingly the generated in vivo and in vitro data is used to

develop predictive models and we see a further move towards in silico methods or e-

ADME [8]. However, there is still some doubt that this will happen rapidly, since ADME

scientists have historically not been an early adopter of databases and computational

modeling tools [6].

Drug-likeness

A number of studies have been performed trying to distinguish typical drugs from other

organic chemicals [9]. Neural networks or decision trees have used to classify these two

distinct sets of compounds. In most approaches, a level of 80% correct prediction was

obtained. Recently the PASS program has been trained for the same purpose [10]. A

similar objective had the analysis of the World Drug Index (WDI) leading to Lipinski’s

rule-of 5 [11]. Each of these studies, and others not cited here, point to the most

important physicochemical properties making up a good drug as far as currently

understood. Also, these are then typically the properties used to construct predictive

ADME/tox models and form the basis for what has been called property-based design

[12].

FIGURE 1 ADME approaches: from low (in vivo ) to high (in cerebro ) throughput.

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In Silico Methods in ADME

A deeper understanding of relationships between important ADME parameters and

molecular structure and molecular properties have been used to develop in silico models

allowing early estimates of a number of drug disposition properties [13,14]. Considerable

effort went into the development of models for the prediction of oral absorption [15–17] and

uptake into the brain [18,19]. More recently, the first attempts have been published to predict

bioavailability directly from molecular structure [20,21]. However, this may not be an easy

task since here we see a superposition of two processes, namely absorption and first-pass

metabolism. In silico approaches in metabolism can be divided in QSAR and 3D-QSAR [22]

studies, protein and pharmacophore models [23], and predictive databases. The potential use

of artificial intelligence and expert systems to select candidate drugs has been discussed in

Ref. [24]. Also absorption simulation and advanced full physiologically-based pharmaco-

kinetic (PBPK) models [25] can be counted to this category.

WEBSITES RELEVANT TO ADME STUDIES

What to Look for on the Internet?

As discussed above, the science of drug metabolism has developed considerably over the last

20 years. Moving from an almost pure bench discipline studying descriptively

pharmacokinetics and metabolism in various animal species and human to more predictive

approaches based on high(er) throughput in vitro and in silico methods. Much is known about

the role of various enzymes in drug metabolism and we are seeing a similar interest now to

understand the role of transporter proteins on the disposition of drugs. The Internet can

potentially be a good source for sharing this progress and emerging knowledge. In silico

approaches rely on the use of molecular properties to correlate these with biological

endpoints such as the percentage of orally absorbed drug. What does the Internet offer on on-

line property calculators? Are there collections of pharmacokinetic (PK) data in the Internet?

If yes, how reliable are these? Who is responsible for the quality control? Another area of

interest is the availability of PK and PK/PD software. Where would starters in the field find

good software?

The objective of current paper is the review a number of websites related to ADME and to

address above questions and to discuss their potential use in academic and/or industrial

setting. Part of the texts below are cited directly from the websites discussed.

Our interest in ADME-related Internet resources has been focused on the following topics:

— Metabolism

— Biochemical pathways

— CYPs (P450s)

— Metabolite schemes

— Transporters

— Human transporters

— P-glycoprotein

— Bioavailability

— Bioavailability data

— Oral absorption

— Blood–brain barrier

— Skin uptake

— Plasma protein binding

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— Pharmacokinetics and Pharmacodynamics

— Software

— Prediction

— Courses

— Pharmacokinetic/pharmacodynamic modeling

— Molecular properties

— Physicochemical

— Calculated molecular descriptors

— Data analysis

— QSAR tools

— Neural networks

The list below is an orientation and should not be considered as exhaustive or complete.

Due to the dynamic nature of the Internet, the lifecycle of some of the currently available

sites may be too short to be relevant. Therefore careful updating of Internet resources links is

important when considering the web as a reliable source for support.

Metabolism

Cytochrome P450

There are several sites which have large collections of P450 related information. The site

of Kirill Degtyarenko has been available for many years and is updated regularly. Some of

the many features are lists of P450 researchers as well as paper, review and Internet site

of the week [26]. Part of the information is mirrored from the site of David Nelson

(University of Memphis), which is also updated regularly [27] and contains a vast amount

of data on P450s. The International Society for the Study of Xenobiotics is maintaining a

site [28] highlighting recent publications and conferences of interest. Nomenclature issues

regarding P450s can be found on-line as well. All reported alleles for P450s are available

including references [29]. An extensive database describing dug interactions is maintained

by David Flockhart (Georgetown University). This site contains a large collection of data

on drug–drug interactions [30,31]. An P450 on-line database is maintained at the Institute

for Biomedical Chemistry and Center for Molecular Design in Russia [32]. Using this site

for the first time, one is prompted to download a Java-component. Unfortunately, this site

is rather slow. Sites of companies such as Camitro [33] and Gentest [34] have interesting

background reading, and the latter is offering the Rendic data collection of P450

substrates to download.

Biochemical Pathways

Kyoto Encyclopedia of Genes and Genomes (KEGG) is an effort to computerize current

knowledge of molecular and cellular biology in terms of the information pathways that

consist of interacting molecules or genes and to provide links from the gene catalogs

produced by genome sequencing projects. The KEGG project is undertaken in the

Bioinformatics Center, Institute for Chemical Research, Kyoto University with supports

from the Ministry of Education, Culture, Sports, Science and Technology and the Japan

Society for the Promotion of Science [35]. This site offers links to various databases and

pathways. The well-known Boehringer Mannheim (now Roche) Biochemical Pathways can

be found on the Internet [36].

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Transporters

Human Transporter Proteins

A human transporter database (HMTD) has been build at the University of California in the

laboratory of Wolfgang Sadee [37,38]. Their objective is to facilitate the study of drug

transporters on a broad scale, including the use of microarray technology. The database

contains information on more than 250 human membrane transporters, such as sequence,

gene family, structure, function, substrate, tissue distribution and genetic disorders associated

with transporter polymorphism. Readers are invited to submit additional data. The University

of Groningen is maintaining a suite of websites related to transporters [39,40]. These include

e.g. hepatic transporters and diseases related to transporter dysfunction [41] and a list of 51

human ATP-binding cassette transporters [42]. Genomic information on individual

transporters can be found in databases such as SwissProt, e.g. human OATP [42].

The distribution of known and putative polytopic cytoplasmic membrane transport

proteins was determined bioinformatically for all organisms for which completely sequenced

genomes were available [43]. Transport systems for each organism were classified according

to (1) putative membrane topology, (2) protein family, (3) bioenergetics and (4) substrate

specificities. The overall transport capabilities of each organism were thereby estimated. The

number of transporters identified in each organism varied dramatically, but was

approximately proportional to genome size. Complete lists of the transporters from each

organism are provided from the pull down menus on this page.

P-glycoprotein (P-gp)

P-glycoprotein is to date by far the most studied transporter and several good sites can be

found. Links to many other P-gp sites are available from a collection at Washington School of

Medicine [44]. A good collection of substrates, inhibitors and inducers of several important

transporters, including P-gp [45], can be found on Paul Watkins site at the University of

North Carolina [46].

Bioavailability and Oral Absorption

Several companies have developed software to predict and/or simulate oral absorption [47–

49]. This is not strictly speaking freely available, but their websites can be helpful to

understand basics of oral drug absorption and how to use this information to build robust

models.

Oral absorption is often estimated by measuring the flux through monolayers of cell lines

such as Caco-2 and MDCK. Many CROs offer this service and on their websites some

background reading may be helpful [50]. Some nice images of MDCK cells can be found at

Ref. [51]. However, this site seems no longer maintained.

The MedChem/BioByte QSAR database is a collection of published QSAR studies in

various fields. The database can be searched on e.g. absorption [52]. All references and

QSARs on absorption can thus be found on-line, which is a highly valuable start to work in a

certain area.

Pharmacokinetics (PK) and Pharmacodynamics (PD)

And good start is the site named Pharmacokinetic and Pharmacodynamic Resources [53].

The purpose of this page is to provide links to information about the discipline of

pharmacokinetics and pharmacodynamics.

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PK Software

An overview of available PK software has been made by David Bourne [54].

A set of 75 pharmacokinetics equations can be downloaded and some demo software for

free from the SummitPK site [55].

Web PK Computing

Concentration-time profiles can be drawn using a site at the University of Utrecht [56]. The

USC Laboratory of Applied Pharmacokinetics has a web computing homepage with access

to various PK calculations [57].

PK/PD Modeling

Some applications of PK/PD modeling can be found on the website of the group of Meindert

Danhof at the Leiden/Amsterdam Center for Drug Research [58]. The Pharmacokinetics and

Pharmacodynamics Software Server at the VA Palo Alto Health Care System offers access to

PK/PD software, such as NONMEM and Xpose and has links to various other useful data

modeling tools [59]. The Biomedical Simulations Resource at the USC has made available a

program called ADAPT [60]. ADAPT consists of high-level programs for simulation,

parameter estimation and sample schedule design, developed primarily for pharmacokinetic

and pharmacodynamic modeling and data analysis applications. It is a computational tool for

basic and clinical research scientists involved in therapeutic drug development, designed to

facilitate the discovery, exploration and application of the underlying pharmacokinetic and

pharmacodynamic properties of drugs. ADAPT has been developed under the direction of

David Z. D’Argenio in collaboration with Alan Schumitzky.

PBPK Modeling

Physiologically-based pharmacokinetic (PBPK) modeling is a tool to predict plasma–time

curves using a set of compartment simulating events in the body. The Center for Applied

Pharmacokinetic Research at the University of Manchester headed by Brian Houston has a

PBPK database and PBPK knowledgebase [61].

PK Courses

Since at many universities little formal training in pharmacokinetics is offered, this is often

learned on the job and through participation in specialized courses. The Internet can play an

important role here by offering such courses and by offering a number of training exercises.

A good start is A First Course in Pharmacokinetics and Biopharmaceutics by David Bourne,

PhD [62]. The USC Laboratory of Applied Pharmacokinetics has a good teaching site on

basic pharmacokinetics, related responses, and clinical applications [63].

Molecular Properties

Solubility, Lipophilicity, Molar Refractivity

Prediction of water solubility and lipophilicity can be performed via the Internet using well-

known algorithms. On the Daylight site CLOGP [64] and CMR (molar refractivity) [65] can

be computed for single molecules using SMILES input. Via the BioByte [66] or Pomona

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College [67] a list with CLOGP values for 140 drugs can be consulted. The details of the

calculation can be browsed.

The Virtual Library project by Igor Tetko at the University of Lausanne [68] offers

calculation of log P (lipophilicity of neutral species) and log S (solubility). The calculation

of this index is based on atom-type electrotopological state (E-state) indices and neural

network modeling. The accuracy of the log P program measured using the leave-one-out

method was 0.46 (1754 compounds) log units [69]. The structures of the analyzed

compounds are coded using SMILE line notation. A user is usually allowed to perform

analysis of a single compound. Such analysis provides a detailed information including

both basic set of 36 E-state and as well as the extended indices elaborated by the authors.

The experimental values of the compounds from the training and test sets are also

provided. The batch mode is available for the registered user and allows to analyze any

number of compounds. Both extended and summary outputs are available for the batch

mode calculations.

Another site for log P and solubility is presented by Interactive Analysis and called

log P [70]. Just enter the SMILES code or mol file of an organic chemical in the

appropriate box. The analysis uses MolConnZ indices and a neural network algorithm

developed using 13,000 accurately measured log P values of known chemicals. The

solubility module log W is designed for neutral organic compounds without ionizable

groups, i.e. a limited set of drugs. Alternatively, log P values can be calculated on the site

of Syracuse Research Corp [71].

Water solubility of a number of organic chemicals, including some drugs, can be found in

the AquaSol database maintained by Samuel Yalkowsky at the University of Arizona [72].

This database will soon be available on-line on subscription.

More Descriptors

QikProp has been developed by Bill Jorgensen at Yale University, specifically for drug

discovery [73]. QikProp results, based on two-dimensional and three-dimensional

descriptors reflecting Monte Carlo simulation studies as well as experiment, have been

fitted to data sets of drug-like molecules, such as oral absorption and brain uptake.

Access to many more molecular descriptors can be obtained via the Dragon software (free

download) [74] developed in Roberto Todeschini’s laboratory at the University of Milano–

Bicocca [75]. The software Dragon v1.11-2001 calculates 853 molecular descriptors,

distributed in 14 blocks:

constitutional descriptors (56 different types)

Topological descriptors (69)

Molecular walk counts (20)

BCUT descriptors (64)

Galvez topological charge indices (21)

Two-dimensional autocorrelations (96)

Charge descriptors (7)

Aromaticity indices (4)

Randic molecular profiles (40)

Geometrical descriptors (18)

Three dimensional-MoRSE descriptors (160)

WHIM descriptors (99)

GETAWAY descriptors (196)

Empirical descriptors (3)

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Data Analysis

QSAR Tools and Neural Networks

Most of the data analysis tools are available in standard workstation or PC software. A

catalog of different statistical resources available on the Internet for deriving structure–

activity models has been made [76]. This includes linear and nonlinear, univariate and

multivariate statistical tools. Also freely available neural network programs have recently

been summarized [76], e.g. the neural network simulator at the University of Tubingen [77].

In some cases the programs can be used directly via the Internet. An example is the site of

Igor Tetko on which he offers various neural network methods to solve problems via the web

[78]. A large collection of computational chemistry sites can be found at Ref. [79] and has

recently been discussed in detail [80]. Several useful chemical software catalogs can be

found, e.g. at the Army Research Laboratory, Major Shared Resource Center (ARL MSRC)

[81] and ChemSW [82].

Other Sites of Interest to Drug Metabolism Scientists

Apart from the here discussed ADME issues there are other topics, which may have valuable

websites and which deserve further evaluation. Among these we could think of e.g. drug–

drug interactions, and food–drug interactions. A list of known food–drug interactions [83]

can be found on The Drug Monitor [84] maintained by Nasr Anaizi.

Summary

Well-known problems with many web sites include their frequency of updating, quality

control, and accessibility. This is also true to the currently listed ADME sites. However, there

appear to be a number of excellent sites, which can be a real help in ADME science, both in

academic institutions, as well as in the industrial labs of pharmas and biotechs. It remains up

to the individual user to follow the evolution of the here-cited web sites on a regular basis and

to base an evolving judgment on the offered quality and usefulness for their daily work. The

potential utility of the Internet is to link ADME sites together. Current contribution is a first

attempt in this direction and hopefully the cited links will be the basis for a true global

network of ADME Internet resources. Hopefully this will lead in the future to productive data

mining via the Internet (web farming).

Acknowledgements

We thank the following people for their contributions to this website collection: S. Anzali

(Merck KGaA, Darmstadt), A. Cleton (Pfizer Global R&D, Ann Arbor), C. Lipinski (Pfizer

Global R&D, Groton).

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