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INTRODUCTION
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2
IN SILICO ADME/Tox PREDICTIONS
DAVID LAGORCE,CHRISTELLE REYNES,ANNE-CLAUDE CAMPROUX,
M
ARIA A. MITEVA,OLIVIER SPERANDIO, AND BRUNO O. VILLOUTREIX
2.1 INTRODUCTION
Partial decline in the productivity of pharmaceutical R&D departments threaten the
sustainability of the current business model. During many years, the drug discovery
process involved chemical synthesis and in vivo testing with optimization of the
compounds pharmacokinetic, metabolic, and toxic properties postponed to later
stages. In recent years, there has been increasing awareness about the importance of
predicting and optimizing the absorption, distribution, metabolism, excretion, and
toxicity (ADME/Tox) properties of chemical compounds along the discovery process
rather than at the final stages. Several in vitro and in silico approaches have been
devised to predict some key ADME/Tox properties. In this chapter, we focus on
methods pertaining to the field of in silico ADME/Tox prediction.
In Section 2.2, we introduce the field of drug discovery and comment on key
computer methods available to carry out ADME/Tox predictions. These encompass
rule-based methods, QSAR (quantitativestructure–activity relationship),and machine
learning approaches among others. In Section 2.3, examples to profile a compound
library using in silico approaches, key commercial and public compound collections,
freely available and commercial computer packages, examples of implementation of
in silico methods in the private sector, and the main statistical methods used in the field
of in silico ADME/Tox profiling are discussed.
ADMET for Medicinal Chemists: A Practical Guide, Edited by Katya Tsaioun and Steven A. Kates
Copyright 2011 John Wiley & Sons, Inc.
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2.2 KEY COMPUTER METHODS FOR ADME/Tox PREDICTIONS
2.2.1 Drug Discovery
Today’s innovative drug discovery projects are very costly and time-consuming, with
very few novel therapeutics making it to the market place [1]. In modern drug
discovery campaign, the process usually starts from an unmet clinical need (disease),
followed by target identification and validation, high-throughput screening (HTS)
and/or in silico–in vitro screening to identify hit compounds, hit-to-lead and lead
optimizations, clinical trials up to the approved drug (Figures 2.1 and 2.2) [2–4].
The overall process takes an average of 12–15 years, costs about $1 billion, and has
a low success rate [5–8]. For example, from an analysis program at Pfizer, over a
10 year period, nearly 100 small molecule discovery campaigns had to be initiated to
result in the identification of a single new chemical entity (NCE, Figure 2.3) [9].
Similar observations were made in all pharmaceutical companies [10]. A study in
the 1990s showed that several reasons may explain why drugs were failing in
development (Table 2.1). At that time, NCEs were essentially dropped because of
poor pharmacokinetic properties, lack of efficacy, and toxicity [11, 12]. In fact,
approximately 75% of the total cost of drug development was attributed to the
Figure 2.1 ADME/Toxconcepts in the context of drug discovery and development.This figure
illustrates the key pharmacokinetic concepts (ADME) that are commonly used in the public and
private sectors during the course of a drug development project. It is also of utmost importance
to consider toxicity and pharmacodynamic events along with the process and, when possible, at
a very early stage of development.
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development failures. The assignment of failure to a distinct category might itself be
misleading, as factors such as efficacy, toxicity, solubility, and pharmacokinetics are
all inter-related. For instance, toxicity might be cited as the ultimate reason for the
termination of a compound in development, but the pertinent factors might actually
be prolonged and unnecessary systemic exposure (i.e., poor pharmacokinetics) or the
Figure 2.3 NCE development requires simultaneous optimization of several parameters and
should bring return on investment. Some of these data can be predicted in silico.
Figure 2.2 The drug discovery funnel. Stages of drug discovery underlining that the process
becomes more and more expensive and that in the early phases, in silico ADME/Toxapproaches
can be used.
KEY COMPUTER METHODS FOR ADME/Tox PREDICTIONS 31
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need to administer high doses because of low pote ncy (i.e., poor efficacy) [13, 14].
Independently of numbers (i.e., values can change according to authors, depending
on the molecules assessed, some authors include all NCEs while others exclude
anti-infective compounds) and as a result of these observations, major initiatives
were undertaken by the pharmaceutical industry in order to address absorption,
distribution, metabolism, excretion, toxicity issues early during the drug discovery
process [14–16].
Today, a promising molecule may still be dismissed at each and every step, starting
from the hit/lead phase, along the preclinical investigations, during clinical trials, and
even years after its successful market launch. The reasons for fai lures are still
manifold: wrong target, poor pharmacokinetics, animal toxicity, lack of clinical
efficacy, contraindication with other drugs, commercial reasons, formulation issues
and most severe, adverse reactions in humans [17]. Recent analysis suggests that over
90% of failures are now due to toxicity, with hepatotoxicity and cardiovascular
implications alone causing two out of three market withdrawals [18]. Termination
of drug development in clinical phases is mainly caused by inadequate efficacy that
supersedes the pharmacokinetic reasons of the 1990s. This may be due to the
increased attention to ADME/Tox related issues during preclinical development.
Yet as mentioned above, it is important to take into account that the assignment of
failure to a distinct category could be misleading. These so-called adverse drug
reactions are gaining broad public attention and rise to a major concern in recent years.
Estimate suggests that every year, about 2 million patients in the United States are
affected by a serious drug reaction, resulting in approximately 100,000 fatalities,
making such events the fourth leading cause of death, not far behind cancer and heart
diseases [19]. Similar numbers have also been estimated for other western countries.
Drug discovery professionals have reacted to these low success rates and economic
pressure toward the development of higher quality compounds, thus, the quest for
ADME/Tox prediction models were initiated and included both, in vitro and in silico
methods [2, 10, 17, 20–54]. One turning point example was due to compounds such as
fluconazole (a triazole antifungal drug used in the treatment and prevention of
superficial and systemic fungal infections, oral drug launched around 1991), which
achieved an excellent balance of potency with ADME/Toxproperties. This compound
was optimized for both potency and ADME/Tox early during the discovery process,
illustrating the advantages of in vitro technologies to reduce time and cost while
TABLE 2.1 Drug Discovery Failures in the 1990s
Drug Discovery in the 1990s
Reasons for Failures Percentage (%)
Toxicity 25
Lack of efficacy 30
Market reasons 5
Pharmacokinetics 40
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developing a “better molecule” [55]. Standard methods to address such problems
in vitro involve the application of preclinical in vitro safety profiling where compounds are tested using in vitro biochemical and cellular assays. Various animal
models are also used. Some of the key events that one would like to estimate are
presented in Figures 2.4–2.6, some of these properties can be predicted using in silico
methods.
In this context and with regard to computer predictions, the well-known rule-of-5
can be seen as a milestone of in silico models, comprising sets of rules, for orally
administered drugs. This pragmatic approach to gain an understanding of the balance
of physical properties that leads to a suitable pharmacokinetic profile for oral
administration was investigated by Lipinski and coworkers [59], at Pfizer, in part
due to observations about the increasing MWand lipophilicity of compounds added to
the HTS screening collections. These authors profiled a range of properties leading to
Figure 2.5 Examples of properties that one wishes to predict (adapted from AbouGharbia [1]).
Figure 2.4 Some current ADME/Tox screening technologies (P-gp, P-glycoprotein; MDCK,
Madin–Darby canine kidney; PAMPA, parallel artificial membrane permeability assay; CYP
450, cytochrome P450 enzymes; Caco-2 cells, immortalized line of heterogeneous human
epithelial colorectal adenocarcinoma cells) [56]. In silico models attempt to predict solubility,
intestinal absorption, plasma protein binding, blood–brain-barrier permeability, metabolism
by individual CYP 450 enzymes, affinity to transporters, renal clearance, among others.
Numerous experimental methods for screening human ADME/Tox properties can be found
in a review by Li including intestinal absorption, drug metabolism, drug–drug interactions, and
toxicity [36, 57].
KEY COMPUTER METHODS FOR ADME/Tox PREDICTIONS 33
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the “rule-of-5.” This work was based on the results of calculated physical properties
on a set of 2245 compounds chosen from the World Drug Index because it was
presumed that they would have suitable solubility and permeability for oral administration. This Pfizer’s team found after cleaning the collection that approximately
90% of the compounds had molecular weight less than 500, calculated log P less than
5, sum of hydrogen bond donors (as a sum of NH and OH) less than 5, and sum of
hydrogen-bond acceptors (as a sum of N and O) less than 10 (note: four rules, not five,
see below). Thus, they proposed that poor absorption and permeation are more likely
when one or more of these limits are exceeded. The approach has been revisited
numerous times and the overall output of these new investigations supports Lipinski’s
findings, although anti-infectious drugs, some anticancer drugs, and natural compounds tend to escape these rules [9, 60]. Further investigations led to the lead-like
concept (rule-of-3: MW < 300, log P < 3) [61, 62].
Although this type of analysis and concepts about compound library profiling are
still under debates, several studies suggest that a paradigm shift in drug discovery has
occurred. The key challenge is now to include in most design and drug discovery
campaigns (high-throughput and in silico screening) and whenever appropriate
depending on the target/disease types, potency, selectivity and ADME/Tox properties
and thus to simultaneously optimize binding affinity/selectivity, pharmacokinetic
properties while avoiding toxicity (Figure 2.7). Comparing property profiles of
development and marketed oral drugs certainly can assist the process. Increasing
the numbers of available and validated test sets for in silico computations should also
help in this endeavor. Pharmacokinetic and the pharmacodynamic profiles are
complex functions of prope rties; however, one might expect that at some points,
after years of chemogenomics and systems chemical biology research, the drug
discovery community will fully understand relationships between physical properties
of compounds and in vitro–in vivo behaviors [9, 60, 63–71].
Figure 2.6 Overview of key ADME/Tox processes. Various events that would need to be
estimated in vitro–in vivo or predicted (see Beresford et al. [58] and Eddershaw et al. [13]).
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2.2.2 Applying or Not ADME/Tox Predictions, Divided Opinions
Academic research groups and pharmaceutical industries are both interested in
reducing the risk of failure during drug development [58]. ADME/Tox modeling
can certainly play a role in this process but it is also reasonable to question how
reliable these in silico predictions are. Considering the complexity of the human body
and the fact that some chemical reactions are not fully understood yet, some scientists
suggest that deriving rules based upon simple descriptors (molecular weight, number
of hydrogen bond acceptors and donors, etc.) or complex ones cannot allow for a good
appreciation of the therapeutic potential of a chemical compound. When complex
statistical methods are used to develop in silico predictive filters, it can be argued that
the training sets are
.
not sufficiently diverse,
.
too large with too many outliers,
.
too small, not sufficiently validated, or
.
that the model is not interpretable by medicinal chemists,
and therefore of little use for finding solu tions through the next rounds of chemical
syntheses [24, 25, 72–76]. Clearly, as with all reductionist approaches, simplifications
introduced in in silico modeling (even in in vitro) of ADME/Tox properties are clearly
imperfect. Proponents of in silico methods claim when properly used that it is possible
Figure 2.7 Paradigm shift in drug discovery. For many years, the attention was focused on
potency with emphasis on ADME/Tox variables at the end of the process (jagged line). In this
approach, potency is optimized first with a realization that ADME/Toxproperties are declining.
The process is repeated many times before hopefully finding an optimum in multiple property
spaces [64]. Currently, it is suggested to optimize both in parallel, potency, and ADME/Tox
properties (straight gray line).
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to do much better even with simple rules than a seasoned medicinal chemist.
Opponents point out that there are many exceptions to the rules and that the fate
of xenobiotics in the human body cannot be predicted accurately. It is understandable
that some scientists believe that when screening a compound collection with in silico
high-throughput ADME/Tox prediction models , there is always a chance that the
next blockbuster drug be disregarded from the list. Yet, the cost for optimization of
molecules containing “nasty groups” must certainly be considered even though the
burden eventually may fall on patients (high dosage, intravenous route, etc.). Also, the
strategy will depend on the stage and nature of the project
.
if one is looking for a hit or is trying to optimize molecules,
.
if the goal is to probe a molecular function,
.
if the project aims at inhibiting protein–protein or other types of macromolecular
interactions.
As such, one may decide to screen all compounds, assuming that medicinal
chemists and formulation scientists can solve the problem later. Conversely, one
might argue that hit molecules should be simple, clean, and as much as possible free of
reactive functional groups (Figure 2.8).
The debate about experimental and in silico artifacts in ADME/Tox profiling, who
and which approaches are right and who is wrong, is not closed [2, 25, 34, 46, 77]. As
pointed out by Beresford et al., there will be mistakes with the example of the Pfizer
Ltd antihypertensive amlodipine drug that has made billions of dollar s and is highly
bioavailable; however, it is predicted to be poorly bioavailable though QSAR
modeling [25]. Yet, it is important to note that the risk of incorrectly classifying a
compound as good or bad based on an in silico predictive model may not be greater
than the risk inherent from using in vitro or in vivo tests, since these ones also have
limitations. A possible solution to this conundrum could be to remove compounds
from a screening collection that are too far away from a softly defined drug-like space
(e.g., high MW usually not appropriate for oral administration) and to flag molecules
with problematic groups without removing them (e.g., compound with a nitro group,
see below). Certainly, avoiding some chemicals or functional groups in preclinical
discovery does not guarantee success, but most likely including them increases the
Figure 2.8 Polemics about applying predictive ADME/Tox models to filter compound
collections or prior to synthesis.
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chances of problems later in clinical development and beyond. Several studies
continue to show that the compounds lacking the rule-of-5 compliance have low
chances of survival [9]. Although not all medicinal chemists will consider the exact
same substructures as problematic [78], some chemical functions have to be avoided
while most people agree that a good lead molecule has low MW and log P and
chemical features that are amenable to optimization. In general, clear risks are
associated with selecting compounds for the development that possess poorer
physicochemical properties.
DEFINITIONS
The term ADME originally referred to as in vivo processes that govern pharmacokinetics (PK). It has been expended to include in vitro physicochemical and
biochemical properties that affect in vivo PK. In general, suitable ADME
properties are essential to both success of in vitro assays and of in vivo studies.
Pharmacokinetic is a branch of pharmacology dedicated to the determination
of the fate of substances administered externally to a living organism. Pharmacokinetic is often studied in conjunction with pharmacodynamics. Pharmacodynamics explores what a drug does to the body, whereas pharmacokinetics explores
what the biological system does to the drug [79]. Pharmacokinetics includes the
study of the mechanisms of absorption and distribution of an administered drug,
the rate at which a drug action begins and the duration of the effect, the chemical
changes of the substanc e in the body (e.g., by enzymes), and the effects and routes
of excretion (of the metabolites) of the drug.
Among the ADME/Tox issues, absorption, distribution, and excretion are
relevant to the journey of the compound from the application point to the site
of action. The other issues, metabolism and toxicity can be considered of a
somewhat different nature in terms of mechanisms and can be treated separately
although all these issues are inter-related. The structure and physical properties of
a compound determine its PK behavior in the body. ADME/Tox plays a major role
in the design of new molecular entities (NMEs, warning, NMEs can be small
molecules, as discussed in this chapter, but the term also applies to therapeutic
proteins and even viral delivery agents). The ADME/Tox methods presented here
cannot be used for peptides or therapeutic proteins, while numerous challenges
are associated with these molecules e.g., peptides can bind to many proteins, are
usually unstable with short in vivo half-life and usually require large or frequent
dosages, in general they can not be given orally [80].
The term off-target is commonly used in the ADME/Tox field. Off-target
activity is often referred to as activity of a particular compound toward a target that
was not anticipated during the design and that can be beneficial or detrimental
(e.g., sildenafil or Viagra, was initially developed by Pfizer to treat angina, but its
side effect on male volunteers led to a change in the therapeutic area of the drug).
However, this term also means antitarget activity, and binding to antitargets is
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