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medicinal chemistry design challenges

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results of an analysis of 349 studies on 315 compounds covering ... versus calculated enthalpy. for reference: 2 kcals = 26-fold off. 4.2 kcals = 1000-fold off ... – PowerPoint PPT presentation

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Title: medicinal chemistry design challenges


1
medicinal chemistry design challenges
  • chemically intelligent data mining
  • multiparameter optimisation for medicinal
    chemists
  • how to handle petabytes of data Google
    Chemistry!
  • activity prediction
  • Dr Tony Wood
  • VP, Head of Worldwide Medicinal Chemistry
  • Pfizer Global Research and Development
  • anthony.wood_at_pfizer.com

2
the challenge for design?
  • 2002-2005 the primary causes of attrition were
    safety and pharmacology

3
in vivo toxicity
  • results of an analysis of 349 studies on 315
    compounds covering 90 targets at 985 doses with
    gt10,000 organ evaluations in 4 species
  • PK known for all cases with strong correlation
    between AUC and Cmax
  • compound set has similar diversity to Pfizer file

4
toxicity threshold selection
  • exposure thresholds were chosen to obtain a
    balance of toxicity/non-toxicity.
  • set to 10uM for the total-drug threshold.
  • approx 40 of evaluations above threshold 40
    below.
  • similar analysis for free drug levels gives a
    threshold of 1 uM.

5
TPSA and clogP are key
  • the y-axis here is a generalized odds, i.e., the
    ratio of the probability of a compound with a
    given parameter value being toxic to the
    probability of it not being toxic

6
toxicity odds
  • combining low TPSA and high cLogP exacerbates the
    risk
  • (numbers in parentheses indicate number of
    outcomes in database)
  • holds for both free-drug or total-drug thresholds

ratio of toxic to non-toxic outcomes
7
toxicity and promiscuity
ratio of promiscuous to non-promiscuous compounds
  • promiscuity defined as gt50 activity in gt2
    Bioprint assay out of a set of 48 (selected for
    data coverage only)

8
clogP and organ toxicity
does a good cell viability profile increase the
probability of a compound being a CNS CAN w/o
organ tox in the clogp risky group (clogpgt3)?
20
23
15
39
39
ClogP gt 3
25
60
22
2
22
5
14
organ tox or not attrition CNS CANs set
ClogP lt 3
50
50
82
Organ Tox
No Organ Tox
9
DEREK
  • a place to store toxicological knowledge
  • knowledge-based expert system
  • broad range of toxicity endpoints covered
  • identifies structural alert
  • provides literature-based rationale for
    prediction
  • qualitative or semi-quantitative predictions
  • now has an API for integration into 3rd party
    software products

10
whats in DEREK?
  • main strengths are mutagenicity, chromosome
    damage, carcinogenicity and skin sensitization
  • some recent efforts in hepatotoxicity and
    teratogenicity

11
challenge 1
  • these relationships were determined using a small
    well characterised data set
  • much more data lies in non curated data sets with
    no structure keys
  • we need chemically intelligent data mining to
    derive knowledge including SAR from this resource

12
properties of CNS drugs
90 0.36
95 Range
13
CNS MPO summary
  • for design (prospective, accurate and constant)
  • increasing CNS MPO enhances the probability of
    candidate survival and alignment of in-vitro

ClogP
C,P,S
Permeability (including efflux)
TPSA
P,S
ClogD
C,S
CNS MPO Desirability
Clearance
MW
P
Safety (including high risk space)
HBD
P
pKa
P,S
Drugs
CANs
14
challenge 2
  • design is now based on a probabilistic basis
    using complex MPO relationships
  • we need transparent easy to construct and
    understand methods to perform multiparameter
    optimisation

15
chemoinformatic predictions
Serine proteases
Cysteine proteases
Ion Channels
Kinases
GPCRs (others classes A, B C)
Aspartyl proteases
Phosphodiesterases
Aminergic GPCRs
Peptide GPCRs
Metalloproteases
Enzymes (hydrolases, transferases, oxidoreductase
s others)
Nuclear hormone receptors
Miscellaneous
node target edge compound
16
Bayesian learning
Rev Thomas Bayes ca 1702 - 1761
data set (assay data)
  • fingerprints are calculated for each molecule
  • check how often fingerprint bit is observed and
    how often in good compound
  • assign weighting factor taking into account both
    activity ratio and sampling size
  • for instance good/total ratio of 90/100 is
    statistically more relevant than 9/10
  • model distinguishes good from bad
  • predict likelihood molecule is good

good actives
bad inactives
fingerprint bits substructures
Bayesian model
17
mining large data sets (HTS)
18
predicting promiscuity
  • 238k actives
  • (? 10 ?M) human target
  • mw lt 1000
  • pass reactivity filter
  • ? 10 actives / target

3870 compounds with 10,806 predictions
90 / 214k
FCFP_6
698 models
Bayesian score
19
searching virtual space
BIG LEAP searching the Pfizer liquid and virtual
compound collections
derive Bayesian model that distinguishes library
1 from 2, from 3, etc
search only these libraries, in real and virtual
compound space
20
BIG LEAP
  • model is built from synthesized compounds (yellow
    squares)
  • nearly all fingerprint features of any virtual
    compound (square marked with ?) are shared by
    at least one compound from the training set
    (squares marked with X)
  • virtual products in areas 1 share at least one
    monomer with a compound from the training set-for
    compound O, the new monomer B2 is very close to
    previously used B1
  • compounds from area 2 can be considered outside
    the scope of the model because they have few
    fingerprint features in common with the existing
    products as shown for compound where monomers
    A2 and B4 are unlike previously used monomers

21
a new series for PRA
22
CCT services
  • a framework for computational scientists to
    publish services (protocols, models) that can be
    immediately leveraged by project teams
  • a knowledge repository for Computational
    Scientists to capture and share their best
    practices

when protocols are published they are
automatically wrapped as new PLP component
23
ligand idea generators
24
uncharted chemical space
25
challenge 3
  • we are not short of idea generators!
  • easy to construct vast virtual libraries
  • we need ways of rapidly scoring and searching
    petabytes of data

26
HERG binding model
  • training set 98,155 compounds (80)
  • talidation set 19,577 compounds (20)
  • test set 9,241 compounds
  • training Kappa 0.61, Concordance 80
  • training Sensitivity 81, Selectivity 80
  • test Kappa 0.46, Concordance 74
  • test Sensitivity 75, Selectivity 74

Grey zone, uncertain prediction
prediction is checked against activity of at
least 3 nearest neighbours to generate additional
confidence measure
27
HLM stability model
  • statistical fingerprint-based model (FCFP-6,
    Scitegic)
  • unstable well predicted, stable not

Unstable Stable
HLM stability experiment Stable Moderately
stable Unstable
Experiment
Stable Unstable
Prediction
28
V1a design for stability
short t1/2
100
Series 1 Series 2
80
In-vitro Clearance
40
20
long t1/2
0
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
CLOGP
most compounds stable within this cLogP range
29
LiPE and LE are quality indicators
linker replacement
needed
aryl switch
side chain deletion/replacement
V1a Ki 780 nM MW 334 cLogP 1.0 t1/2 HLM 120
mins
V1a Ki 28 nM MW 441 cLogP 5.5 t1/2 HLM 6
mins (Human Liver Microsomes)
-1.4 log (IC50)
LE (Ligand Efficiency)
how efficient each heavy atom is Class
dependent…0.3 0.5
n Heavy Atoms
how efficient each lipophilic fragment is
LiPE (Lipophilic Efficiency)
-log (IC50) - cLogP
30
using LiPE to view SAR
31
predicting activity
DG(expt)
plot of experimental affinity versus calculated
enthalpy for reference 2 kcals 26-fold
off 4.2 kcals 1000-fold off
DltUWgt
kcal/mol
  • "Improving Accuracy in Protein-Ligand Affinity
    Calculations"
  • Paper 104, ACS meeting in Philadelphia (Aug
    2004)
  • Michael K. Gilson, Center for Advanced Research
    in Biotechnology,
  • Rockville, MD

32
the source of the problem?
15 to -25 kcal 15 to -25 kcal
-5 to -10 kcals
  • we usually focus on the interactions DltUWgt
  • we always neglect TDSconfig
  • we count on cancellation of errors within series,
    or other corrections, which leads to scattered
    data.

force field (CHARMM, AMBER, etc.) van der
Waals Coulombic Hydrogen-bonding surface area
term Hydrophobicity/organophilicity generalize
d Born/Poisson-Boltzmann Desolvation of polar
groups, Coulomb screening
potential energy
solvation
sampling/Sum over energy wells Preorganization/S
train Entropy losses on binding (rotational,
translational, conformational)
flexibility, entropy terms
33
challenge 4
  • we are not short of idea generators!
  • easy to construct vast virtual libraries
  • we need more accurate activity prediction to
    allow filtering and selection

34
knowledge management
35
data access tools
36
learning culture
37
web2 technologies
  • build project teams around Sharepoint/OneNote
  • implement a RSS strategy around Newsgator
  • create a literature knowledge sharing culture
  • use Wiki type technology to share knowledge
  • Pfizerpedia

38
thanks to
  • BSA
  • David Price
  • Simon Bailey
  • Julian Blagg
  • Nigel Greene
  • activity prediction
  • Marcel de Groot
  • Martin Edwards
  • Alex Alex
  • Jeff Howe
  • Ben Burke
  • CNS MPO
  • Patrick Verhoest
  • Travis Wager
  • Anabella Villalobos
  • Spiros Liras
  • VLS
  • Giai Paolini
  • Willem Van Hoorn
  • Enoch Huang
  • Jeff Howe
  • web 2
  • Jerry Lanfear
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