Literature Seminar (M2)

Can we predict good drugs ?
the application of～ big database to R&D of drugs ～Nature 2012, 486, 361.

10th November 2012 (Sat.)
Takuya MatsumotoIntroduction 1

Combined FDA-approved new molecular entities (NMEs) versus R&D spending for the top
nine largest pharmaceutical companies.
NME: a drug that contains no active moiety that has been approved by the FDA in any otherapplication submitted. NME includes biologicals and vaccines.
Nature Chem. Biol. 2011, 7, 335.

Introduction 2

Only 4% survivedNMEs success rate by phase. Combined R&D survival by development phase forthe top fourteen largest pharmaceutical.
Approval data is based on approval of NME by a regulatory authority in a major
market (EU, US or Japan).
Nature Chem. Biol. 2011, 7, 335.

Introduction 3
Phase III and submission failures: 2007–2010

 Next to lack of efficacy, adverse drug reactions (ADRs) are the leading cause
for attrition in clinical trials of new drugs.
 Some ADRs are caused by modulation of the primary target of a drug, others
result from non-specific interactions of reactive metabolites.
In many cases, however, ADRs are caused by unintended activity at off-targets.
Nature Rev. Drug Discov. 2011, 10, 87.

Introduction 4OOOMeSO
VIOXX (refecoxib)

Selective COX-2 inhibitor
cardiovascular event(mechanism is still unknown)

Appetite suppressant
One of its metabolites, norfenfluramine
activated the 5-HT2B receptor, leading toproliferative valvular heart disease.

Cyclooxygenase (COX) has two well-studied isoforms, called COX-1 and COX-2.
COX-1 mediates the synthesis of prostaglandins responsible for protection of the stomachlining, while COX-2 mediates the synthesis of prostaglandins responsible for pain andinflammation. By creating "selective" NSAIDs that inhibit COX-2, but not COX-1, the same
pain relief as traditional NSAIDs is offered, but with greatly reduced risk of fatal ordebilitating peptic ulcers.

Today’s topic
Predicting unintended off-target toxicity by informatics methods !
Informatics: the science of information
It studies the representation, processing, and communication of informationin natural and artificial systems.
Bioinformatics (DNA/RNA, protein, etc.)

Chemoinformatics (ligand, drugs)

What’s SEA ?
SEA = the Similarity Ensemble Approach
the total number ofProtein: 246
Ligand: 65,241(Pair: 5.07 x 109 ≒ 65,2412)

the total similarity score between protein A (ligand α & β) & B (ligand 1 &2)= 0.2 + 2 ×0.3 + 0.5 = 1.3 (the raw similarity score)
1. Score the similarity of all ligands pairs between two proteins with Tanimoto efficient (Tc)
(0 (complete dissimilarity) < Tc < 1 (identity))
2. Sum up all Tc score (the raw similarity score)
3. Calculate expectation values and evaluate the similarity of two proteins
Nature Biothechnology 2007, 2, 197.

Tanimoto coefficient (Tc) 1
Basic idea

ExamplesTanimoto coefficient (Tc) 2

The enzymes thymidylate synthase (TS) anddihydrofolate reductase (DHFR) both
recognize folic acid derivatives and are
inhibited by antifolate drugs. Despite this,two enzymes have no substantial sequence
identity and are structurally unrelated.
Thrombin: serine protease protein(unrelated to DHFR)

 For most ligand pairs the Tc was low, in the 0.2 to 0.3 range (insubstantial similarity).
This was true even when comparing a set to itself.
DHFR versus DHFR (red), 80.4% (0.1 to 0.4 range), 4.7% (0.6–1.0 range) and 0.5% (1.0)
 The raw similarity score: the sum of ligand pair Tcs over all pairs with Tc ≥ 0.57
Nature Biothechnology 2007, 2, 197.

Quantifying SEA
P = 1- exp(Kmne-λχ) where χ is similarity score
χ is the raw similarity score (?)

The smaller, the more similar

Folic acid

Nature Biothechnology 2007, 2, 197.

Patterns of similarity

 On average, any given receptor wassimilar to only 5.8 other receptors withan expectation value <10−10.

 Clusters of biologically related targets may
be observed, as no explicit biologicalinformation, only ligand information, isused to calculate the cross-target similarity.
Nature Biothechnology 2007, 2, 197.

Comparison of sequence and ligand-based protein similarity

Red : pairs with strong ligand-set similarity but weaker sequence similarity.
Dark gray : pairs with strong sequence similarity but comparatively lower ligand-set similarity
White : pairs where pharmacological and sequence similarity approaches agree(either positively or negatively)(a) overall difference heat map(b) folate-recognition enzymes and adenosine-binding enzymes(c) GPCRs, ion channels and nuclear hormone receptors
Nature Biothechnology 2007, 2, 197.

Predicting and testing drug promiscuity
Methadone is known to have dual specificity for μ-opioid receptors and NMDA.

Antagonism of M3 muscarinic receptors by the μopioid agonist methadone in a direct binding assay

Antagonism of M3 muscarinic receptors
by functional assay

Nature Biothechnology 2007, 2, 197.

Small Summary 1
SEA = the Similarity Ensemble ApproachPredicting new molecular targets for known drugs
Drugs
878
FDA-approved drugs

+

2,787
investigational drugs=3,665
total

Data basesame as the previous study (Nature Biothechnology 2007, 2, 197.)
246 proteins(65,241 ligands)
40 pairs
known

Total

30 pairs
tested23 pairs
K i < 15 M

901,590 protein-drug pairs (3,665 x 246)

Result901,590 protein-drug pairs
SEA
6,928 pairs
with E-values better than 1 x 10-10

184 pairs
sampling
3,832 pairs
unkown, biologically interesting

Nature 2009, 462, 175.

New targets as primary sites of action

 Used since the 1950s as an antihistamine, Fabahistin is now being investigatedfor Alzheimer’s disease.
 Fabahistin binds predicted new, off-target(5-HT5A receptor) much stronger
than its canonical H1 receptor target.
 Its activity against 5-HT5A and related serotonergic receptors may have
implications for Fabahistin’s role as an Alzheimer’s disease therapeutic.
Nature 2009, 462, 175.

Off-targets as side-effect mediators

 Motilium achieves peak plasma concentrations (Cmax) of 2.8 µM after
intravenous administration.
 This formulation was withdrawn owing to adverse cardiovascular effects, with
the FDA citing cardiac arrest, arrhythmias and sudden death.
 Although Motilium binds the hERG potassium ion channel with a half-maximum
inhibitory concentration (IC50) of 5 µM, the 71–710nM affinities observed here
against α1A, α1B and α1D may also contribute to these cardiovascular effects.
Nature 2009, 462, 175.

Drug binding across major protein boundaries
78th target

167th target

 The protein target with the highest sequence similarity to any of a drug’s known
targets is rarely predicted by the SEA approach.
 Rather, the target predicted by ligand similarity is typically well down in thesequence-similarity ranking.

Nature 2009, 462, 175.

Small Summary 2
3,665 (FDA)-approved and investigational
drugs were compared against 246 targets.
364 additional off-targets for 158 drugs arepredicted with E-values better than 1x10-50,whereas 1,853 new off-targets are predictedwith E-values better than 1x10-10.
This compares to the only 972 off-target
activities already annotated in the databases.
Prediction includes some interesting new offtargets such as;(1) the new targets contribute to the primaryactivity of the drug(2) the new targets may mediate drug sideeffects(3) the new targets are unrelated bysequence, structure and function to thecanonical targets.
Nature 2009, 462, 175.

Large-scale prediction and testing of drug
activity on side-effect targets 1
1. Calculate E-value by SEA methods, predict new drug-off-target andconfirm by in vitro experiment(similar as the previous study (Nature 2009, 462, 175.))
2. Quantify the relationships between protein targets and adverse
drug reactions (ADRs) by the use of enrichment factor (EF)(different point from the previous study)
3. Create a drug–target–ADR network

Nature 2012, 486, 361.

Large-scale prediction and testing of drug
activity on side-effect targets 21Drugs & Targets
656
FDA-approved drugs

listed in ChEMBLx=73
with established association of ADRs,
for which assays were available at Novartis

47,888
totalSEA
1,644 pairs
with E-values better than 1 x 10-4
403 pairs
listed in ChEMBL

694 pairs
tested at Novartis
sampling

1,241 pairs

893 pairs
348 pairs
listed in other data base

Nature 2012, 486, 361.

New drug-off-target predictions

1

Nature 2012, 486, 361.

1

SEA or 1NN
1NN = one-nearest neighbor model

Model

SEA

1NN

Hit rate

2/3

2/4

Adjusted hit rate = (number of true positives+1)/(number of total predictions+1)(number of total prediction = number of true positive + number of false positive)
Nature 2012, 486, 361.

Are new off-targets predictable ?

1

Of the 151 (ref. slide 21) new off-target predictions, 39 (26%) had BLAST Evalues greater (worse) than 10-5, suggesting the previously known targets
shared no sequence similarity with the new off-targets.
Nature 2012, 486, 361.

Associating in vitro targets with ADRs
Drugs

SEA

the total number oftarget-ADR pairs

relationships between drugs and targets
to assess the potential clinicalrelevance of the discovered targetsof drugs systematically...

known fromdata baseTargets

a quantitative score that associated invitro activity with patient ADRs(a score between targets and ADRs)

solution: Enrichment Factor (EF)

ADRs2

●
●ADR 3
ADR 2
ADR 1

Drug A
TargetTargetDrug Bknown from Target
Drug C data base●at least 9 (3x3)●● possible Target●
ADR pairs

EF = p/(AxT/P)
in which p is the co-occurrence of target X and ADR Y, A is the number of times ADR Y waslinked to any drug–target pair, T is the number of times target X was linked with any drug–
ADR pair, and P is the total number of target–ADR pairs.
45 drugs (p) which have the ADR epigastralgia and interact with COX-1
6,046 drug–target pairs (A) where the ADR epigastralgia r was linked with
2,188 drug–ADR pairs (T) where COX-1 was linked with
681,797 target–ADR pairs overall (P)Thus the pair epigastralgia–COX-1 was enriched 2.3-fold above random
Nature 2012, 486, 361.

2New, confirmed targets associated with ADRs Of the 151 confirmed (ref. slide 21) new drug–target associations tested atNovartis, 82 were significantly associated with one or more ADR, resulting in a
total of 247 drug–target–ADR links.
 In 116 cases, the enrichment factor (EF) of the new drug–target–ADR link wasstronger than that for any previously known target.
Nature 2012, 486, 361.

Drug–Target–ADR network

3

Grey: known targets
Blue: newly predicted targets
Orange, Red: ADRs associated with eachtargets (Red: ADRs are significantly (EF>1)
associated with the new off-targets.)(Targets related by sequence areconnected by grey edges.)
Nature 2012, 486, 361.

Demonstration of an association in an
accepted in vivo biomarker

This was the first example that a synthetic steroid acted on COX-1 enzyme !
Nature 2012, 486, 361.

Target promiscuity

Nature 2012, 486, 361.

Drug promiscuity

Nature 2012, 486, 361.

Summary
SEA = the Similarity Ensemble Approach
EF = Enrichment Factor
Drug

SEA Protein
ligand 1
ligand 2
●
●

EFADR

Drug-Target-ADR network

ADR 1
ADR 2
●
●

Nature 2012, 486, 361.

Comments
 Only some side effects fall into the remit of this approach, which assumes anoff-target mechanism.
 Almost 46% of the predicted drug–target associations were disproved, butthey were just as often confirmed by experimental ways.
 The method was used automatically at scale, without human intervention.
Pragmatically, the ability to calculate drug–target–ADR networks provides atool to anticipate liabilities among candidate drugs being advanced towards
the clinic, or yet earlier, for prioritization of chemotypes in preclinical series.
 The use of Big Data will be dramatically accelerated in almost all fields.Appendix

How to measure “Drug-Likeness” ?
a new measure taking the place of‘the Lipinski’s Rule of Five’
Nature Chem. 2012, 4, 90.

Appendix

Oral drug & Lipinski's rule of five
Oral drug is the best, thus the most important way to dose drugs.
empirical criteria whether a small organic
molecule is suitable for a oral drug

Lipinski's rule of five
Lipinski's rule of five Its molecular weight is less than 500.
 The compound's lipophilicity, expressed as a quantity known as logP (the logarithm of thepartition coefficient between water and 1-octanol), is less than 5.
 The number of groups in the molecule that can donate hydrogen atoms to hydrogenbonds (usually the sum of hydroxyl and amine groups in a drug molecule) is less than 5.
 The number of groups that can accept hydrogen atoms to form hydrogen bonds(estimated by the sum of oxygen and nitrogen atoms) is less than 10.
Nature 2012, 481, 455.

Appendix

The Implementation of Rules
The rules are only predictive of oral bioavailability (the absorption by passive diffusionof compounds through cell membranes).
Due to their simplicity, the rules are widely used by medicinal chemists to predict not
only the absorption of compounds, as Lipinski originally intended, but also overall druglikeness.
Despite Lipinski’s recommendation that the rule be considered as a guideline, in reality
it is used routinely to filter libraries of compounds. The implementation of rules asfilters means that no discrimination is achieved beyond a qualitative pass or fail—allcompounds that comply with the rules are considered equal, as are all that breachthem.

Nature 2012, 481, 455.

Appendix

Quantifying drug-likenessTo quantify compound quality, the concept of desirability was applied to provide aquantitative metric for assessing drug-likeness, which we call the quantitative estimateof drug-likeness (QED). QED values can range from 0 (all properties unfavorable) to 1 (allproperties favorable).

Weighted QED

Desirability takes multiple numerical or categoricalparameters measured on different scales and describes each by
an individual desirability function. These are then integrated intoa single dimensionless score. In the case of compounds, a seriesof desirability functions d are derived, each of which corresponds
to a different molecular descriptor. Combining the individual
desirability functions into the QED is achieved by taking thegeometric mean of the individual functions, as shown in equation.

Asymmetric Double Sigmoidal (ADS) functions

Mr: molecular weight(a - f: constant values)

Nature Chem. 2012, 4, 90.

Appendix

Histograms of selected properties

Histograms of eight selected molecular properties for a set of 771 orally absorbed small
molecule drugs.
molecular weight Mr (a), lipophilicity estimated by atom-based prediction of ALOGP (b),number of HBDs (c), number of HBAs (d), PSA (e), number of ROTBs (f), number of AROMs(g) and number of ALERTS (h).
The Lipinski-compliant areas are shown in pale blue in (a), (b), (c) and (d).
Nature Chem. 2012, 4, 90.

Appendix

Prediction of the drug-likeness of proteins’ligand

Not all ligand-binding sites have the appropriate physicochemical and topologicalproperties to bind small-molecule drugs non-covalently with sufficient affinity.
Binding sites that do have these characteristics are described as druggable (this definition
is independent of any wider biological considerations).
QED provides an efficient means to quantify and rank the druggability of targets according
to the chemical attractiveness of their associated ligands.
In other words, proteins whose ligands had the highest QED scores should be the mostchemically tractable targets for drug discovery, because their known ligands are the mostdrug-like.
Nature Chem. 2012, 4, 90.

Appendix

Structural diversity networks(a) a target for which the associated bioactive compounds are neither drug-like nor diverse(b) a target for which the associated bioactive compounds are diverse, but not drug-like(c) a target for which the associated bioactive compounds are drug-like, but not diverse(d) a target for which the associated bioactive compounds are both drug-like and diverse
In each of the networks compounds are represented as nodes and are coloured by theirrespective QED values. An edge connects nodes if they are structurally similar (defined by aTanimoto coefficient≥ 0.7).

Nature Chem. 2012, 4, 90.

Appendix

Top human targets by three different ranking schemes

Left | ranking targets by the mean QED of their associated ligands
Center | ranking targets by the mean of the most drug-like active series (clusters)
Right | ranking targets by the degree of enrichment of drug-like series (type (d) target in the previous slide)(targets are ranked by the proportion of active series that have a mean QED above that of the top 10% of
the ChEMBL database (0.796)) .
The mean QED for all targets in the list is 0.478. For the targets of approved drugs the mean QED is 0.492
and for the targets of approved oral drugs the mean QED is 0.539 . Drug targets are, indeed, enriched
towards the more highly desirable targets, with 70% of the drug targets found in the top 50% of theprioritized target list.

Nature Chem. 2012, 4, 90.