Changes in the Profiles of Drug Properties: An Experimental, Computational and Informatics Perspecti

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Changes in the Profiles of Drug Properties An
Experimental, Computational and Informatics
Perspective

• Christopher A. Lipinski
• Pfizer Central Research
• Groton, CT 06340, USA

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Outline of This Talk

• ADME is rate determining rather than lead
  finding
• Experimental solubility in discovery and
  development
• Calculated trends in newer v.s. drug-like
  compounds
• Physico-chemical property trends depend on the
  research approach, Merck v.s. Pfizer candidates
• HTS hits, computational profile and experimental
  solubility
• Causes for computational trends at Pfizer, Groton
• Causes of poor aqueous solubility
• Effectiveness of computational filters

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Oral absorption problems (in-vitro to Candidate
stage) can be more costly in manning and less
predictable than the - to Lead and
optimization of Lead-in-Vitro stages
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Aqueous Solubility and Permeability Data Must be
Provided to Chemistry as Early as Possible to
Avoid Oral Absorption Problems
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Minimum Acceptable Solubility in ug/mL Bars shows
the minimum solubility for low, medium and high
permeability (Ka) at a clinical dose. The middle
3 bars are for a 1 mg/Kg dose. With medium
permeability you need 52 ug/mL solubility.
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The logic for a turbidimetric solubility assay

• Solubility in Discovery Solubility in
  Development
• turbidimetric solubility thermodynamic
  solubility
• non crystalline crystalline
• solids not characterized polymorphs
  characterized
• solubilized in DMSO solubility measured by
  solid
• added to stirred gavage equilibrating with
  aqueous medium medium
• 10s of minutes time scale 24 to 48 hours time
  scale
• used for early in-vivo SAR used for minimum
  absorbable dose, dissolution, salt selection
• correlation with in-vivo correlation with
  clinical dosage animal SAR form
• better in early discovery essential in
  development

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Turbidimetric Solubility in a Flow Cell. lt 20
ug/mL is poor solubility.

• Used to measure solubility at 5-65 ug/mL for
  poorly soluble heterocyclic compounds

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Computationally comparing libraries. Drug-like
v.s. new drugs

• Use the presence of an INN name or a USAN name or
  marketed status as a flag for a compound with
  drug-like properties
• 7483 Drugs with INN name, USAN name or approved
  for marketing
• Compare to 2679 New Drugs from the Derwent World
  Drug Index
• mechanism field - trial preparations
• No CAS registry number, no INN/USAN name,
  abstracted in 1997, 1998, 1999

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Distribution Parameters for 7483 INN/USAN Drugs
Define the 90 Limits Corresponding to Properties
Unfavorable for Oral Drug Absorption.
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The Rule of Five mnemonic.

• Poor absorption or permeation are more likely
  when there are
• More than 5 H-bond donors.
• The MWT is over 500.
• The CLogP is over 5 (or MLogP is over 4.15).
• The sum of Ns and Os is over 10.
• Substrates for transporters and natural products
  are exceptions.

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How Pfizer Uses the Rule of 5

• On-line alert at compound registration
• Filter for HTS screening library
• Filter for purchased compounds
• Criteria for focused library synthesis
• Candidate nomination guideline

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Newer drugs (blue) are higher molecular weight
than drug-like compounds (green).
Molecular Weight
2679 Newer drugs, 7484 Drug-like compounds
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Newer drugs (blue) are less permeable than
drug-like compounds (green) as calculated with
polar surface area.
Polar Surface Area
2679 Newer drugs, 7484 Drug-like compounds
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Newer drugs (blue) have more rotatable bonds than
drug-like compounds (green).
Rotatable Bonds
2679 Newer drugs, 7484 Drug-like compounds
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Newer drugs (blue) have higher Andrews Binding
Energy than drug-like compounds (green).
Andrews Binding Energy
2679 Newer drugs, 7484 Drug-like compounds
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Newer drugs (blue) are slightly more lipophilic
than drug-like compounds (green) using the
Moriguchi LogP.
Moriguchi LogP
2679 Newer drugs, 7484 Drug-like compounds
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Upwards molecular weight trend in Merck advanced
candidates.
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Upwards molecular weight trend in Pfizer, Groton
early candidates
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No increase in lipophilicity with time in Merck
advanced candidates.
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Upwards lipophilicity trend with time in Pfizer,
Groton early candidates.
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Increasing hydrogen bond acceptor trend with time
in Merck candidates
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No hydrogen bond acceptor trend with time in
Pfizer, Groton candidates.
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Lead Approach, Permeability and Solubility

• Structure Based
• MWT up
• peptidomimetic
• fit 3 or more sites
• H-Bonding up
• fit _H bond sites
• fit salt bridges
• LogP no change
• no selection pressure
• Poor Permeablity

• HTS Based
• MWT up
• HTS selects for larger size
• LogP up
• HTS selects for high LogP
• H-bonding no change
• no selection pressure
• Poor Solubility

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Leads at Pfizer changed towards higher molecular
weight and lipophilicity with the advent of high
throughput screening.
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Early high throughput screening hits (blue) were
more lipophilic than phase-2 (green) or marketed
drugs (red).
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Filtering improved computational HTS hit
profiles, but solubility of HTS hits could still
be low (data from 633 Pfizer HTS hits)
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78.5 Percent of phase II compounds (INN, USAN or
NCE) have good aqueous solubility (gt 65 ug/mL).
Only 14.2 percent have poor solubility (data
based on 1597 solubility assays on phase II
compounds)
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31.2 Percent of 2246 commercially purchased
compounds have very low aqueous solubility (lt 20
ug/mL), incompatible with appreciable oral
activity
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39.8 Percent of 33093 medicinal chemistry
compounds have very low aqueous solubility (lt 20
ug/mL).
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Summing Up - Understanding the solubility problem

• Traditional Drug Discovery
• tight link to oral activity testing
• 10-15 poor solubility
• Single Compound Traditional Synthesis
• no link to oral activity testing
• isolated by crystallization
• 30 poor solubility
• Single Compound Lead Optimization
• traditional in-vitro optimization
• additional 10 poor solubility
• Combinatorial Chemistry ??

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Effectiveness of our computational intervention
strategy is measured by the decrease in our Rule
of 5 alerts
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Summing Up, Pfizers Experimental, Computational
and Informatics Perspective

• Drug property profiles are best improved by a
  combination of
• computational filtering initiatives
• discovery focused experimental assays
• cross departmental, people based, educational
  initiatives
• data base mining approaches